Mobile Applications and Delivery of Bioinformatics Education in the Post

University of Nevada, Reno

Mobile Applications and Delivery of Bioinformatics Education

in the Post-Secondary Introductory Biological Science

Classroom

A dissertation submitted in partial fulfillment of the requirement for consideration of a

Doctor of Philosophy in Education

Joseph Wilcox

Dr. David Crowther, Advisor

December, 2020

THE GRADUATE SCHOOL

We recommend that the dissertation prepared under our supervision by

JOSEPH WILCOX entitled Mobile Applications and Delivery of Bioinformatics Education in the Post-Secondary Introductory Biological Science Classroom be accepted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

David Crowther, PhD Advisor Elizabeth de los Santos, PhD Committee Member

Robert Quinn, PhD Committee Member

Leping Liu, PhD Committee Member

David Quilici, PhD Graduate School Representative

David W. Zeh, Ph.D., Dean Graduate School

December, 2020

Abstract

The field of life science is facing a great challenge, in that the tools are beginning to outpace the user. Technology has rapidly been accelerating while the knowledge to use this new tech has been unable to keep pace. For example: as the recent push to get the faster, cheaper sequencing machine designed has inspired engineers to design contraptions that achieve the original idea but also more complexity, greater amounts of data, and the need for scientists to learn new techniques to understand it all. In this case, by generating large amounts of data in a short amount of time, the ability to interpret the produced numbers becomes increasingly important (Bender, 2015; Frankel & Reid,

2008). Additionally, acquiring access to the tools necessary to interpret data can prove a challenge. While many of these analytical algorithms can be found on the internet or through peer-sharing networks (in the case of specific programs written for the exact task at hand), these are somewhat restricted to the home or office, as they require the user to be at a desktop or laptop computer.

This dissertation’s aim is to design a suite of mobile applications that would allow a scientist, a student, a casual researcher, or anyone with a set of data they wish to examine the ability to do so while out of the home or office, as well as test the use of this technology with students in a classroom setting. By integrating facets of informatics research such as math, life science, statistics, and computer science, the user will be able to manipulate and view their dataset from the coffee shop, the passenger seat, the airport, or even the hallway. The Application was designed in a modular format so that the developer can add new operations and techniques that also allows a flexibility in the ii program to bring in other developers’, researchers’, and students’ ideas, creating a crowdsourced, collaborative endeavor.

The main thrust of this design is its integration into the undergraduate classroom.

Bringing these analytical tools to the future researchers can open their minds to possibilities they may never have considered. By using this App in workshops, lessons, and units that currently exist within the undergraduate life science curriculum, the students received a hands-on experience they may not be privy to otherwise; whether this is due to availability of technology, access to the technology if it is available, or simply being more proficient in a tool they use daily. The App based on the probability behind sequence similarity (described within) combines both statistics and life science, making it ideal for integration into the class, while the App based on CRISPR techniques combines bioinformatics, biostatistics, and life science concepts, as well as introduces a current and very relevant topic that has potential to impact the world of science and public policy. In addition, secondary schools (high schools) can use this tool to bring in rudimentary concepts. Using their own mobile devices gave a more personal touch to the users’ data analysis and interpretation, and thus yielded a better understanding of the topics.

Utilizing current programming tools, current mobile technologies, and scientific backgrounds in conjunction with a series of workshops designed both from a

Constructivist combined with a Connectivist approach, it is shown students will not only learn the information and topics presented to them, but enjoy the act of doing so, and help foster a love of life-long learning. iii

Methods included workshops designed to instruct in these Biological, Statistical, and

Molecular Biological topics, which were then evaluated prior to and upon completion of these workshops. Using a pre- and post-test format, as well as a Likert-scale survey, students were evaluated on their prior knowledge, the knowledge gained throughout, and the attitudes of utilizing mobile technology.

Results showed that there was a significant difference in the knowledge learned and retained through usage of the mobile applications vs traditional computing

(desktop/laptop) methods, with the resultant statistical analysis showing a p<0.001, and the threshold at 0.05. Student attitudes also showed that they learned more, and desired not only the subject matter be incorporated, but also the mobile technology within their current and future coursework.

Overall, this project cemented the tremendous potential utilizing mobile technology within the classroom poses, in addition showcasing how the future of learning could be more virtual than traditional. Considering the current state of affairs with the global

Coronavirus pandemic, with an increasing amount of public and private schools going virtual/remote, the need for more, better, tailored, engaging, and connected scientific material for these types of learning scenarios is higher than ever before.

Dedicated to Catherine Schmader & Eric S. Gustafson

Without their support and inspiration, I would not be here.

Acknowledgements

David Crowther, PhD Leping Liu, PhD Robert Quinn, PhD Elizabeth de los Santos, PhD David Quilici, PhD Karen Schlauch, PhD Laura Briggs, PhD Josh Baker, PhD Patricia Berninsone, PhD David Shintani, PhD Juli Petereit, PhD Julie Ellsworth, PhD David Zeh, PhD John Cushman, PhD Patricia Ellison, PhD Adeel Tariq, MS Every one of my students (whom kept me going) All the authors I researched All who attended my workshops All of the people I annoyed for help

Table of Contents

Abstract i

Table of Contents vi

List of Tables xi

List of Figures xii

Introduction/Background 1

I. Introduction 1

II. Background 2

a. Current Position of Bioinformatics and Mobile 4

Analysis

b. Android Vs. Apple 5

c. Bioinformatics Background 9

d. Educational Background 14

III. Theoretical Foundation 19

IV. Research Questions and Hypotheses 20

Literature Review 22

I. Integration of Electronic Technology into the Classroom 22

Environment

a. Early Technologies, integrations, and attitudes 23

b. Current technologies, attitudes, and integrations 24

c. Technologies in the College Environment 26 vii

II. Science Needed by Participants 29

a. Biology 29

i. Basic Chemistry 30

ii. Molecular Biology 32

1. DNA 33

2. RNA 35

3. Proteins 35

iii. Bioinformatics and Genomics 38

b. Summary 38

III. Mobile Applications 39

a. Application Design Review 39

i. Development of Application Design 40

b. Science Governing Individual Applications and Sub- 48

Applications

i. App 1: Test for Normality 48

ii. App 2: Principal Component Analysis 50

iii. App 3: Probability Behind Sequence Similarity 54

iv. App 4: Simple CRISPR Analysis 60

IV. Learning Theories 64

a. Types of Learning Theories 64

b. Constructivism 68

i. Principles of Constructivism 69

ii. Constructivist Classroom 73 viii

c. Connectivism 76

i. Connectivist Classroom 79

V. Instructional Design 84

VI. Summary 87

Methods 89

I. Type of Study 89

II. Researcher’s Background 89

III. Participants 91

a. Sample Size 92

IV. Data Collection 94

a. Design of Individual Application Suites and Alpha Testing 95

i. Normality 95

ii. Principal Component Analysis 96

iii. Probability Behind Sequence Similarity 99

iv. Simple CRISPR Analysis 100

v. Beta Testing 103

b. Workshop Curricula 105

i. Implementation Phase I 108

ii. Implementation Phase II 109

iii. Control Group 112

c. Surveys 113

d. Technology Used 114 ix

e. Coronavirus Interruption 115

V. Data Analysis 116

a. Assessment of the Implementation 116

b. Assessment Control Group 117

c. Identifiers 118

VI. Limitations to the Methods 119

a. Sampling 119

b. Participation 119

Results 121

I. Overview of Research Project 121

II. Data Analysis 123

a. Statistical Analyses 123

b. t-Test Analysis 124

c. Surveys 128

III. Ancillary Analyses 134

IV. Participant Flow 134

V. Baseline Data 135

VI. Statistics and Data Analysis 135

VII. Adverse Events 135

Discussion/Conclusion 137

I. Mobile Technology in the Introductory Biology Classroom 137

a. Mobile Applications Vs Traditional Computing 138 x

b. Attitudes of Mobile Applications in the Classroom 142

II. Discussion on the Methods and Implementation 145

a. Post-Pilot Program 146

i. Coronavirus Interruption and Adaptations 148

III. Relevance of Study to Research 150

a. In-Person Classroom Setup 151

b. Online Classroom Setup 153

c. Bioinformatics 155

i. Normality 155

ii. Principal Component Analysis 155

iii. Probability of Sequence Similarity 156

iv. Simple CRISPR Analysis 156

IV. Limitations of the Implementation 157

a. Control Group 157

b. Instructor 159

c. Time 159

V. Conclusion 160

VI. Future Directions 161

References 165

List of Tables

Table 1. The Twenty Amino Acids (a.a.), Their Abbreviations, and Their Letter Representations 36 Table 2. Group Statistics of the Independent t-Test. 125 Table 3. Results of Independent t-test Run on Differences Between Pre- and Post-Test Scores. 125 Table 4. Mann-Whitney U Test Results of the Differences in Pre- and Post-Test Scores 128 Table 5. Frequency of Responses by Question of Likert-Scale Survey for Control Group. 129 Table 6. Frequency of Responses by Question of Likert-scale Survey for the App Group. 130 Table 7. Group-Specific Questions from the After-Workshop Survey 144

xii

List of Figures

Figure 1. Worldwide preferences of mobile Operating Systems 6

Figure 2. Worldwide preference of the Android Operating Systems 7

Figure 3. Increase in technology vs lowered cost to sequence a genome. 12

Figure 4. Structure of a DNA Nucleotide 34

Figure 5. Structure of an Amino Acid 37

Figure 6. The Normal Distribution and a Q-Q Plot in R. 49

Figure 7. Visual Representation of how PCA functions. 51

Figure 8. PCA displaying population data of two colors of birds, separated by color.

Figure 9. The binomial formula. 55

Figure 10. NCBI BLAST Main Page 58

Figure 11. BLAST Interface and Results. 60

Figure 12. CRISPR Overview. 61

Figure 13. CRISPR/Cas9 Activity 63

Figure 14. The ADDIE Instructional Design Model. 87

Figure 15. G*Power Display of Required Sample Size. 93

Figure 16. SCIpac Central Hub and Interface with Google Drive 95

Figure 17. Test for Normality Interface and Results Screen(s). 96

Figure 18. Principal Component Analysis Results Screen. 98

Figure 19. Probability of Sequence Similarity and BLAST Results Screens. 100

Figure 20. CRISPR Input Screens and Results 103 xiii

Figure 21. Comparison Between the Means of Differences in Pre- and Post-Test Results.

126

Figure 22. Normality Between Participant Groups. 127

Introduction

This research project focuses on the creation, development, and implementation of a suite of mobile applications designed to analyze biological and biomedical data on- the-go. For years, there have been multiple applications available both online as well as on physical media (disks, drives, etc.) that allow researchers, students, and armchair scientists access to analyze their numbers, no matter how large or small. The goal of this proposal is to put that ability in the palm of their hands – literally.

The primary aim of this dissertation is to design and test several user-friendly mobile applications to allow researchers, business professionals, casual scientists, or anyone with a set of data to analyze to do so on the move. With the almost exponential advancement of technology, this will enable researchers to be much more flexible when working with their data, and this project will take advantage of that. The design of the application will be developed modularly, so that additional analytical modules and features can be added easily, with the end goal a suite of well-used tools within data science research.

In addition, the suite of applications will be tested and utilized in the classroom environment through implementation of workshops designed to facilitate the learning and retention of biomedical, bioinformatical, and statistical topics. These topics may have been introduced to students previously, however the goal is to educate those who might have never heard of these subjects prior, and through the usage of user-friendly interactive mobile applications, demonstrate that anybody can learn and/or perform science and scientific analyses, regardless of one’s access to a laboratory. With mobile phones, tablets, pads, and watches available and accessible to all walks of life and 2 geographic locations, and that reach growing almost exponentially every year, this is a field ripe for implementation and research.

Background

There have been multiple applications and algorithms developed for online analytical implementation over the last few decades. With the explosion of computing power in the late 1970s and early 1980s and the further expansion of the internet and the ‘information superhighway’, scientists had a universe of possibilities in their research, analysis, and publications. Early developments were BLAST, that allows sequences to be catalogued, researched, compared, and analyzed using the framework of the then-emerging NCBI

(Altschul, Gish, Miller, Myers, & Lipman, 1990). This was performed through physical

(external) discs that the researcher used to access the database with the Entrez portal.

While this was better than nothing at the time, it was cumbersome, slow, and dependent on disc location and keeping discs free from scratches. Other programs were developed over the years, and with the advent of high-speed internet there were many new tools that researchers and developers could share over their network connections as well as upload data to be analyzed almost immediately.

Some of these programs are still available today (UniProt, ExPASy, SwissDB, etc)

(Biasini et al., 2014; Gasteiger et al., 2003; Wu, Apweiler, Bairoch, 2006) and are readily used for sequence analysis. They are reliable, updated, and effective for database queries, sequence structure approximation, or comparisons of unknown sequences against model organisms. With the spike in high-throughput and then next-generation sequencing technology, these types of applications became more prevalent, popular, and necessary. 3

Shortly thereafter, browser-based programs such as Clustal Omega, T-REX, MAFFT, K-

Align, and others emerged (Katoh, Misawa, Kuma, & Miyata, 2002; Lassmann &

Sonnhammer, 2005; Makarenkov, 2001; Thompson, Higgins, & Gibson, 1994). These programs allow for the retrieval, analysis, and presentation of data in formats that were easily downloadable and transferable to other media. By generating comprehensive evolutionary (phylogenetic) trees, gene relatedness across and within species could be studied, enabling hypothesis-generation never before feasible.

With the advent of the smartphone circa 2006, programs were developed for these devices. Being called “apps”, short for applications, these programs ran the gamut from simple painting to calculating to altering photos by giving the user a moustache. These apps exploded in popularity and the saying “There’s an App for that” became almost as common as saying “Gesundheit”. Apps for running, for dieting, for music, for exploring databases of movie stars, everything one could possibly think of had its own app. Except one major arena - science; for years the idea of having applications pertaining to science seemed a sort of anathema. Current online searches revealed only forum posts from four years ago pointing out the “obvious” obstacles in producing such apps, and there are only a handful relating to science, and many of those are either database-focused (Eleftheriou,

Bourdakou, Athanasiadis, & Spyrou, 2015), library/term-oriented (Oluwagbemi,

Adewumi, & Esuruoso, 2012), or games designed around learning scientific techniques or predicting rudimentary structures. An examination of the top scientific apps produces these results:

 BrainPOP! – An interactive quiz game about introductory science topics 4

 Nasa APP – A virtual tour of the main facility as well as a peek into what the

society is doing at the moment

 Mitosis and Molecules – 2 separate apps that offer the ability for users to rotate

and manipulate both molecules and the steps of mitosis in a 3D environment

 Table of Elements HD – An interactive Periodic Table

 The VSB library of science topics – A series of apps that offer visual lessons in

topics such as Physics, Chemistry, and Biology

 SkyView – An interactive map of the constellations, updated regularly and

synched to the user’s location

 3D Brain – A 3-dimensional map of the brain that users can click on and learn

about the various parts.

Current Position of Bioinformatics and Mobile Analysis

Bioinformatics is still a new science, with new facets constantly emerging, and along with the advances comes with its own set of problems. One problem is data manipulation, analysis, visualization and presentation. While many of the programs mentioned earlier do the job well and there are also numerous ways of doing so using classical programming techniques and languages such as R, SAS and Python, it would be best served to be able to perform at least some of these processes on a device not anchored to a wall or desk.

What subjects make up the definition of Bioinformatics may differ between journal, lab and/or country, but what can typically be considered as pillars of the subject are:

Computer Science (programming), Biostatistics (math), and Life Science (sequence 5 analysis and structural/functional prediction). Apps are typically found in the programming arm; however, those apps are designed primarily for educational purposes – learning and application of the actual language – and not analysis. There are also a handful of statistical and biostatistical apps out there, but those mainly deal with simple calculations such as Fisher’s Exact Test, Chi-Squared tests, the Z-test, and others involving the standard 2x2 table in statistical analysis. Many of these apps, as with other subjects, also have multiple database, educational, and test-prep purposes. When it comes to life sciences, there are only a handful Apps that involve analyses, and those are rudimentary (at best). Only one (1) found is in anyway analytical (DNAApp) (Nguyen,

Verma, & Gan, 2014), and it uses a simple BLAST-style algorithm to compare small sequences of DNA in the ab1 format, which is used exclusively with equipment produced by Applied Biosystems, thereby radically limiting the range of this tool’s use. It can produce a FASTA file upon completion of analysis/comparison, but is limited to one technological platform only. Other life science apps are once again databases, educational question-answer types, test-preps, dictionaries, or simple games. Searching for

“Bioinformatics Apps” on any site or mobile store returns many of the ones mentioned above, and then multiple random apps that are tangentially related to the split-up words

“Bio”, “apps” and “informatics”.

Android Vs Apple

This project was developed on the Android Operating System for phones and tablets produced by companies such as Samsung, LG, and Motorola. Why utilize this platform instead of the more seemingly popular iPhone? Aside from the United States and a select 6 few other countries, the Android OS is utilized and prioritized much more than iOS.

Figure 1 shows the breakdown in Operating System usage of many major brands across the world. As can be seen, the Android platform is preferred at least 4x more than the iOS and incredibly dwarfs the smaller competitors of Windows and Blackberry Operating

Systems.

Figure 1.

Worldwide preferences of mobile Operating Systems (Credit: Statcounter).

The upper, orange line denotes Android; the lower, grey line denotes iOS. Android has a 74.5% market share worldwide as of Dec 2019. Figure 2 presents a distribution of Operating System use per country. As can be seen here, the majority of the world uses Android and prefers it over the US-centric Apple product.

Figure 2.

Worldwide preference of the Android Operating Systems [StatCounter].

Android-preferring countries are represented in green, iOS-preferring countries are grey. Unknown mobile preference is blue. There are some indicators of Apple gaining ground and claiming some of the world- share, however with the death of Steve Jobs on October 5, 2011, the new CEO took the company in a new direction, although it was presented as the direction Jobs wished it to go. This led to the production and distribution of the Apple Watch (Zolfagharifard,

2016); the removal of the headphone jack (with installation of a purely Bluetooth sound system) (Peckham, 2016); the very controversial – not to mention expensive (Currid-

Halkett, 2017) – iPhone X in 2017; and most recently the revelation that the company had been purposely slowing down older hardware in order to force purchase of newer models.

These poorly-received decisions led to a plummet in consumer confidence in the company from which they are still attempting to rebound. 8

Phones and tablets which use the iOS operating system are strictly limited to Apple products. By developing within the Android environment, this App will be accessible to a much broader swath of users.

Android Studio is the official development kit that was designed by engineers at Google and premiered in 2013 with version 0.1 (Durochet, 2013; Google, 2018). It is part of the

Android Application Software Development Kit (SDK) and provides the interface necessary for constructing applications. This kit is available as freeware, which is software that is free, making it extremely cost effective. In terms of programming, the

SDK runs almost exclusively on Java, a programming language that is well established, thus making the development of the Application easier. Google has created a new

Android-specific language named Kotlin; however, that is still very new, and is having a difficult time finding its niche within app programming. The Android SDK currently sits at version 3.1, which will be used for this project.

In terms of operating system versions, there have been many over the years. For example, the current updated version of Android OS (2017) is 8.0 and is termed “Oreo”. Android

7.0 (2016) was labeled “Nougat”, 6.0 (2015) was Marshmallow, 5.0 (2014) was Lollipop,

4.0 had Ice Cream Sandwich (2011), Jelly Bean (2012), and KitKat (2013). Versions 3 and below had more dairy treat and sweets-themed names or none at all (Version 1.0)

(Google, 2018). The app will be designed initially for version 8.0/7/0 but it is desirable to make use of as many of the older Operating Systems (OS) that can be supported. Access should be as open as possible, which is a primary goal of this project. Once completed, upgrades will be maintained for newer versions of the operating system. 9

One of the benefits of using Android Studio 3.1 is that there is a phone emulator program, which simulates an actual Android environment on a handheld device, within. Emulation allows the SDK to generate any previously known phone or tablet layout; for all intents and purposes it is a phone but cannot make or receive calls. This allows for easy testing and real-time debugging of the app in progress. In addition to the virtual environment, the apps were tested on phones/tablets, and not on the emulator, through the usage of the connectivity option within the SDK to the developer’s (and their team’s) own android devices. By placing the phone/tablet into debugging mode, it becomes a valuable tool to assist in development.

Most (if not all) of the design was performed on a 2016 MSI G72 Dominator Pro 220 laptop computer with an Intel i7 6783 CPU @ 3.8GHZ, 32GB of RAM, 8GB NVIDIA

980M graphics card, Windows 10, and over 8TB of storage space, with 500GB of that belonging to a Solid State Drive. This is the “development platform”. This machine was originally designed for high-powered, high-end video gaming, so it had more than enough power at its disposal to design any application.

Bioinformatics Background

Bioinformatics is a new area of scientific exploration, one that opens up newer horizons in data gathering and analysis. Through the advent and increase in computing power over the previous 35 years, the ability to investigate the smallest molecules of life and deduce what they actually perform is a monumental achievement. By increasing the FLOPS

(FLoating OPerations per Second) that a computer’s central processor can calculate in an exponential trend, the power of computing is reaching heights never before imagined 10

(Dolbeau, 2018; Smith, 2011). FLOPS utilize Floating-Point Arithmetic, which is similar to scientific notation, save that the operations are performed using forms of base 2 and not base 10. The coding architecture stores the exponent, with the more popular version

(ANSI/IEEE) using a form of IBM’s original base 16 architecture; this is why most computing terms are expressed in forms such as 32, 64, 128, 256, etc. The higher the

FLOPS, the faster and more capable the processor. Currently, the Chinese Tiahne-2 super computer holds the record at performing 34 quadrillion FLOPS, with the most powerful commercially available processors belonging to the Playstation 4 and Xbox One gaming consoles at around 1.8 and 1.4 billion FLOPS, respectively (Routley, 2017). In regard to mobile processing, the Samsung Galaxy S7 smartphone is currently the powerhouse, being able to perform 1.21 billion FLOPS, and runs on the Android platform.

Moore’s Law is another explanation of this concept. Postulated by Intel Computing co- founder Gordon Moore in 1965, when he noticed that the amount of transistors per square inch of circuit board space had doubled almost every year, he predicted that the trend would continue for the foreseeable future (Moore, 1965). An extension of this applies to the size and speed of these processors and other components. Through technological advancement, the chips themselves can contain much more raw computing power and increase the speed by utilizing multiple different materials as well as linking them in newer ways to allow for parallel and modular processing, increasing the speed (Cavin,

Lugli, & Zhirnov, 2012). This cannot go on indefinitely, however, and some have theorized the summit has already been reached. (Collins, 2017) 11

Genomics and proteomics are important pillars of bioinformatics; they existed before the ability to massively sequence and analyze high-throughput sequence data, but in today’s society they are on a fast track. Extracting, isolating, and sequencing the molecules of life such as DNA, RNA and proteins have become central in many biomedical, pharmacological, pharmacogenetic, and molecular biological studies (Thampi, 2012).

Moore’s Law can also be applied to the technology within this sub-field, as sequencing technology has exponentially exploded over the last ten years. Private companies such as

Illumina, Pacific Biotechnology, and Oxford Nanopore Technologies have produced machines that are capable of reading short and long sets of DNA much more rapidly with an exponential reduction in cost. Through creation of algorithms that can analyze differences in the sequences, their abnormalities, predict their structure and function (in the case of proteins), and comparisons between each other and the organisms they represent, the field has allowed researchers to examine their samples in much better detail than before, reducing the amount of error and increasing the amount of accuracy in conclusions.

Figure 3.

Increase in technology vs lowered cost to sequence a genome. (Credit: NCBI/NIH)

Bioinformatics also encompasses the field of statistics, specifically biostatistics. Going all the way back to Hippocrates and his studies of disease, he examined the factors that may influence the humours, or the forces that were believed to control every human (Miller

1962); this was the first documented example of a researcher looking beyond the subject and seeking out environmental and other outside factors, leading to the creation of epidemiology. Moving into the eighteenth and nineteenth century - with the push to discover treatments for epidemics such as smallpox - the discovery that numbers can substitute and, in many cases, predict patterns within these studies and as such, inoculation of organismal test subjects with these hypothetical treatments would not be entirely necessary. This, in addition to a need to help support Darwin’s theory of natural selection, enabled Karl Pearson to break through with multiple statistical models to use with these hypothetical (and after-experimental analyses) tests (Pearson, 1896, 1900). His contributions, such as the coefficient of correlation, multivariate analysis, the Chi- squared test, standard deviation, the p-value and principal component analysis, were able to give objective, quantitative values to what were before primarily qualitative 13 observations. Applying these statistical models and operations to biological data and models is what constitutes biostatistics (Hardy & Magnello, 2002; Magnello, 2002), and integrating these models into computer-generated hypotheses, models, and analyses then brings it into the fold of bioinformatics.

The final main pillar of bioinformatics is computer science and programming. Detailed earlier within this section, some algorithms are freely available to users to provide cursory analyses, so a researcher can generate general overviews of their research and adjust accordingly if needed. To perform higher-level manipulation and analysis, however, the ability to implement an algorithm or small program to give very specific answers to the question(s) posed is needed. There are many programming languages available, each with its own strengths and weaknesses for a particular approach.

Languages such as Ruby, C/C+/C++/C#, Python, Java, F#, Objective-C, PHP, SQL,

MATLAB, SAS/SPSS, and R, are all adequate for scientific programming. Occasionally one can use older languages such as Visual BASIC, pascal, FORTRAN, and COBOL, although those are limited and primarily used in governmental programming (Irrera,

2017; Konkel, 2013). For bioinformatics, R is considered the ‘gold standard’ with regards to data analysis and visualization. It is a command-line as well as object-oriented programming language; this allows the researcher to get a quick analysis or a much more involved deep look into their numbers through construction of modules that can be accessed multiple time by many commands. Python is a strong contender for second, as its object-oriented design is streamlined for optimal performance, with only a few ways to approach any task. 14

Knowledge of all these disciplines contributes to a better understanding of not only the data and analysis but also helps to spread understanding through an incorporation of the spheres of influence. This project will include some broad aspects of bioinformatics, leading to a comprehensive mobile application that anyone can use to understand the world around them better.

Educational Background

Education – it can be argued – is one of the oldest professions in human history. From the dawn of early man, the ability to pass on knowledge gained from one individual or group to another has been consistently performed, for our own survival as well as personal and intellectual growth. Whether one is lecturing a room of four hundred students on the benefits of Particle Physics at a prestigious university in the present day, or a father teaching his children how to craft a weapon out of a stick and rocks in order to both defend themselves and hunt for dinner, education is at play. As such, there has been many generations of approaches and designs created and honed to fine points in order to broach the topic(s) required for either enrichment or sustenance. This study does not attempt to reinvent any wheels, rather introduce a new technology for use in existing and new curriculum and assess its efficacy within said realm.

Technology itself is a word that can have multiple variations applied to its usage. In recent memory, technology refers to the creation and implementation of electronic devices that can be used for entertainment, information, education, transportation, and many other facets of life. However, technology may be defined as any tool created by humans to make an aspect of their lives easier, better, and/or more innovative. Using this definition, everything from a massive supercomputer crunching petabytes of data at 15 speeds hitherto unknown down to a simple pencil enhance the lives of hundreds to millions every day. In the most recent years, however, our ability to create new electronic technology has increased at rates not even predicted by Moore’s Law, shown earlier, which states that every generation the size of microprocessors will decrease by a factor of

0.5 while their abilities will increase by the same factor. This would predict a model that falls more under a steadier category, with a constant incline at ~45° on any standard

Cartesian plane; what the world has been seeing, however, is much more attuned to an exponential curve, sharply shooting upward in a more 90° arc since the late 1990s. Gone are the days when one would spend hours or even days in a library researching for hours to publish a simple five-page essay for a class, now that type of research can be performed almost instantaneously.

Massive access to information as well as ease of use and accessibility to most users has allowed human educational possibilities to break free of barriers many in the past could not overcome. With this advanced movement, those who would naturally gravitate to the new tech are the younger generation(s); new, flashy ways to communicate and express themselves have always been desired by those in their adolescence and early adult periods of growth. As per the usual, the older generations – those who would be their instructors – are outpaced by the forward momentum and find themselves playing catch- up in the end.

Using technology within the educational sphere has had its benefits as well as challenges.

As stated above educators can be stubborn about their techniques and settle into routines that can be difficult to break; these are typical of human behavior in any sphere of life 16 and/or industrial setting.

While there are numerous approaches, theories, designs, and interventions that have been designed, tested, implemented and/or codified over the numerous years that humanity has been passing knowledge from one group to another, the idea of storing vast amounts of this knowledge and having access to such is something that has only recently been developed. Previously, there were large libraries filled with vast rows and stacks of books that would require half of a lifetime in order to understand, let alone be able to break down and pass on to another human being. With the advent of the internet, and databases, technological servers, hard drives, “the cloud”, and other mass storage options, the ability to generate, store, and access information, knowledge, and history it makes for an unprecedented time within that history to be able to present material to students, whether that be in a formal or informal classroom setting.

In order to take advantage of this time, the creation of toolkits to help these students navigate the stores of knowledge has to attempt it’s best to stay “ahead of the curve”, and offer the students the best possible interaction they can partake. Reiterating a previous statement that people as a whole can be turned off by something they do not understand, they can further be solidified against something by having a poor experience with something they do not understand. So, when creating and implementing these new technologies and procedures into an introductory course/workshop, one must be careful and mindful of the participants’ needs. Creating something that can be used with existing technology (i.e. laptop computers or phones/tablets), making the interface accessible to as many as possible, and delivering these ideas in a way that does not cause friction between the learner and the material is of utmost importance in the coming years. This is already a 17 common practice in classrooms for current methods, curricula, and subjects; it will just be applied to the generation and implementation of mobile technology and programs so the current generations do not feel as if modern education is years behind, and therefore they may feel left behind.

Current methods, while functional and shown to have results both positive and negative, can be adapted to accommodate the new technology and access to information. These prevalent delivery modes of subjects such as math, history, English, social studies, and other disciplines have their benefits, such as social interaction between the instructor and students, the ability for students to develop skills that can serve them in the adult world such as reading comprehension, writing skills, and critical thinking cannot be denied.

Having students sit through lectures, perform activities, solve problems through in-class and home-assigned worksheets, and evaluate their progress through both formative and summative assessments has a track record of providing results, guiding students to their academic goals at least through the primary and secondary educational systems. There are always new interpretations, new approaches, new ideas, new theories, and new implementations of these variations, and those – as well as the instructors/administrators who are the architects of these changes – should be applauded. These can be explained by current learning theories such as Behaviorism, Constructivism, and Cognitivism. These theories, conceived through collaboration and developed over the years to address different perceived needs of students. Any instructor (formal or not) who gets the student to understand an idea, concept, or procedure they did not before entering their tutelage is a success, regardless of what the student or society at large does with that success. 18

What this dissertation has accomplished is to take these new-fangled technologies, create a package of programs/applications, generate an intervention through workshops and curricula additions/changes, and implement with a community college classroom.

Utilizing a relatively new Learning Theory: Connectivism (Siemens, 2005; Downes,

2010), which postulates that using previously developed learning theories can be combined with students’ digital needs within the new sphere. In essence, technology can bring learners together and enhance their experiences through connections between each other, their technology, and the real world. This classroom was an Introduction to

Biology 100-level course, in addition to other 100-200 level life science classes. which means these students are those who may have not taken a science class in a long time, those who are returning to school after a long absence, or those who are actually attempting to learn the subject and are interested to see if this particular genre/subject is to their liking. This is a prime choice for an implementation such as this; with the lack of

Bioinformatics knowledge among the general public, due to the novelty of the subject as described above, throwing this subject at students who have spent their semester learning from the ground-up about general biological topics is advantageous in that their minds are not made up completely at this point. One can introduce the idea of computerized analysis and other statistical/biomedical topics they would not be exposed to in a more

‘normal’ Intro to Bio class, and they can then have more information with which to make up their mind about their future.

Community College students, as described above, are looking for their niche, even if they have been told to go down certain roads (i.e. nursing), so integrating these new topics may help them discover something for which they have an affinity that they may not have 19 considered. Generally, they are attending a Community/Junior college for convenience’s sake (i.e. job schedules), money issues (i.e. cheaper classes overall) and/or combining those into their uncertainty of their future; they are looking for their path. It is hoped that introducing these topics during an introductory course they may have their interest piqued.

Theoretical Foundation

The backbone of this research project will be the theoretical approaches of

Constructivism (Vygotsky, 1978), combined with Connectivism (Siemens, 2005;

Downes, 2010), the former was developed by Lev Vygotsky and the latter George

Siemens and Stephen Downes. These theoretical backgrounds detail that learners are their own champions, basically, in that an instructor is there to introduce a topic, provide a scaffold, and step back to allow the participant/learner to form their own constructions and connections through self-guided learning. This does not entail the complete removal of the instructor from the process, as they act as facilitator, nudging them along in their respective processes, but also realizing when it is necessary to step in and provide more meaningful interaction, background or guidance. Connectivism is a relatively recent theory that has emerged, combining the usage of technology with the idea that students learn on their own, forming their own networks, similar to a computer’s central processing core linking all components of the system, as well as software.

Within the Bioinformatics and science realm, we will be looking at testing for Normality,

Principal Component Analysis, Sequence Similarity (with BLAST), and basic CRISPR analysis. The first two – and a small portion of the third – are grounded within the statistics portion of Bioinformatics, the third and fourth techniques are more within the 20

Molecular Biology aspect of the subject. Both overarching subjects are incorporated into

Bioinformatics research and are intertwined with the subjects of pure Mathematics,

Computer Science, and other Life Science studies. Bioinformatics is the ultimate

‘marriage’ of science, computing, and mathematics.

Research Questions and Hypotheses

With the preceding information, this brings the focus to the questions the research project wishes to have answered throughout implementation of the intervention and technology.

Combining all of the ideas of Statistical Data Screening, Probability, DNA/RNA sequence similarities, and CRISPR gene editing capacities with a comprehensive, interactive, inquiry-based instructional package could have potential to increase not only the knowledge base, but the interest of the budding student researcher. With that in mind, this proposal aims to answer these research questions:

RQ1: Is there a significant difference in the means of learning progress scores in statistics/bioinformatics concepts between Community College General Biology and

Microbiology students who used desktop / laptop computers and those who used the mobile suite?

RQ2: What are the attitudes of Community College General Biology and Microbiology students in regards to the implementation of technology while learning statistics/ bioinformatics concepts and will the technology preferences differ between those students using desktop / laptops versus those using the mobile suite as measured on a Likert scale and open ended survey? 21

The following hypotheses, to be explored throughout the project, were generated from the above research questions:

Hypothesis 1: Utilizing the mobile application will have a significant impact on the learning of Bioinformatics materials within the Introductory Community College Biology

Classroom.

Hypothesis 2: There will be a difference between the attitudes of those who wish to use computers vs mobile devices, with the preference going to mobile.

These questions were answered through development, integration, and application of the curriculum designed by the author/researcher, in conjunction with the mobile suite of applications developed by the researcher/author. Not only did this exemplify the capacity for bringing these tools more into the fold, but it demonstrated that instructors as well as students continue to push the malleable limits of learning.

Literature Review

This literature review will focus on the key areas of technology integration within the classroom, the application design and theories behind the design of each individual sub- app, and the learning theories applied within the research sphere of this project. There are many ways of looking at and targeting the research, especially as this is a borderline multidiscipline study, coming at the education within the introductory college classroom level, and dealing with statistics, biology, genetics, mobile technology and educational approaches. As such, this will look at each of those individually, then wrap them all up in an attempt to display the requirements met for such a study to be performed.

This review will begin with an analysis and review of previous attempts to integrate technology into the classroom environment. This will focus on electronic technologies, many developed within the previous one hundred years, rather than consist of a comprehensive historical review of every technology ever conceived to assist students in their learning. It will then follow with a look at the applications; their design philosophies, the individual apps themselves, and the science research behind their design and implementation within the project. Finally, the review will summarize all of the learning theories searched, evaluated, and applied to this research project.

Integration of Electronic Technology into the Classroom Environment

From lights, to telephones, to radios, to televisions, to computers, and now onto mobile devices, electronic devices have been integrated and implemented throughout recent history to assist in students learning. Without lights, students would not be able to see their instructor or their reading material, although that famously did not stop American 23

President Abraham Lincoln from teaching himself Law by firelight and flickering candlelight as a young boy (Baldwin, 1904). Without radios many people (not only students) would not be aware of world events. Without televisions, these same events would not have images to assist telling these stories. Finally, without computers, these events, knowledge, and interactions would not be as easy as they were with the previous technologies. Computers, tablets, smartphones and watches, and their associated software connects the world in ways early humans could barely imagine previously.

Bringing these advances into the classroom is always a challenge, as generally the younger people are more open to adapting what’s “new and exciting”, while the older generation(s) are a little more hesitant to accepting these ideas (Howard & Mojesko,

2015). We begin with a miniature history of incorporating the technologies of the time into a classroom setting, whether it be used for entertainment or learning.

Early technologies, integrations, and attitudes

Figuring out ways of integrating technology into the school curriculum is not a new problem; anecdotal, but there were those in the late 1800s who could be overheard saying that electricity itself was a “fad” and “wouldn’t last long”; this information comes from an old newspaper read through microfiche in the 1990s, and the author cannot recall the exact paper, dates, and issues. However, one does not need to look far to see how something that is new and possibly assists with easing the lives of people can be met with fear, derision, disbelief, and occasionally outright rage. Radio emerged in the 1930s as a way to not only entertain, but to keep people who could afford one in touch with the common man and current events, but they were rarely used within a classroom setting, 24 until the 1950s or so. Televisions are a common presence in classes at almost every level in today’s day and age, however, there were negative attitudes associated with introducing such a new technology into the classroom environment. A common axiom of those early times was “TV rots your brain”, and the idea that video learning can be beneficial faced an uphill battle. Early studies from the 1970s-1980s showed that there was large concern for declining rates of reading and therefore cognitive abilities

(Corteen, 1986; Robinson, 1972). Previous research into integration within classrooms largely back up this claim (Convery, 1990; Newman, 1981), with one study showing it had an opposing effect on student learning . There was a long held belief in a statistical difference between learning and television time, this caused attitudes to sour towards the new technologies. As iterated above, however, attitudes soon changed – as they often do

– when the technology finds its way into the hearts, minds, and households of most of the populace (Howard & Mozejko, 2015).

Current technologies, attitudes, and integrations

Jumping ahead to the current landscape, we have the advent of smartphones, personal tablets, and wristwatches that can pretty much perform the same function as a desktop or laptop computer. These powerhouses are able to access any sort of information from anywhere in the world, and keep individuals in communication with one another in a myriad of ways; telephone calls, internet emails, and social media through both mobile- browser based programs and applications specifically designed to function on the device’s Operating System (OS). Up until the mid-00s, mobile phones were basically just that: phones, with a few texting technologies that utilized the number pad and its 25 associated letters. When the Apple iPhone was introduced to the public in 2006, it revolutionized the industry and culture as a whole (Campbell & La Pastina, 2010; Rose,

2016). It created the ability for the simple phone that millions of users already enjoyed and turning it into the hub of their lives that we know today. Consequently, the attitudes toward this new technology shifted as users began to realize it could be used for nefarious purposes such as cheating on exams, or causing more distraction than the previous telephone-only option (Ghulam Behlol, 2013; Gupta & Irwin, 2016; Nyamawe &

Mtonyole, 2014; Tindell & Bohlander, 2012).

These attitudes then went from enjoyment and embracing of the new tech and evolved into “Put your phones away as soon as you enter” rules throughout classrooms, mentioned earlier. This comes from both instructors and students, as there has been research to show that having your phone/tablet out at your desk is indeed distracting and causes lapses in learning (Ward, Duke, Gneezy, & Bos, 2017), however there are more positive attitudes that show a distinct difference between teachers who use technology and teachers who resist technology, based on the actual techniques used (AlTameemy,

2017). Research shows that “83.78% of elementary schools, 75.56% of middle schools, and 63.46% of high schools” regulate cell phone use in China (Gao, Yan, Zhao, Pan, &

Mo, 2014), while the United States – the focus of this project – has 69% of the high schools with some type of ban on cell phone use (Common Sense Media, 2009); and 112 high school principals in 44 States have stated 84% of their schools have an actual written policy (Obringer & Coffey, 2007). In one contrasting and supportive study, the researchers found that 78 K-12 teachers held attitudes on using mobile phones for 26 classroom instruction and found that 69% of these teachers supported the use of mobile phones in the classroom and were using them for school-related work (Thomas,

O’Bannon, & Bolton, 2013). So, while perceptions and attitudes may be shifting, this leaves open a large hole where students have these amazingly powerful technological pieces in their pockets, but no ability to actually use them for anything besides social media, email and random internet searches, but NOT IN CLASS!

As implementing new ideas and technologies into the primary or secondary classrooms pose multiple problems, the college classroom can be a fruitful place to begin such studies; the students are generally older – more than not often adults in either early- to middle age – and therefore more responsible and can be trusted with these new ideas.

With the so-called “Net Generation” dubbed so by Karen Worley (2011), and how this generation is composed almost entirely of “Millennials” - a term coined by Gloecker in

Business Week (2008) - meaning those who were born between the years of 1982-2002, the ability to utilize technologies that these kids have grown up with and the instructors maybe use for light work duties at best can be fully brought to bear. With many of these millennials either entering or have already entered the college environment, it might be the best time to introduce these technologies into their learning strategies.

Technologies in the College Environment

Stated above, these phones, tablets, watches, and other various devices used by students on a daily basis have been banned from usage throughout their scholastic career; unless they were in one of those “lucky” schools that allowed such usage (or at least usage with 27 caveats). It would make sense that implementation of these concepts, ideas, and tools in the college classroom would be beneficial.

Going back to Millennial students, it was reported that 69% of students wished to have these devices in their hands within the classroom environment at the middle-school grade levels (Harris Interactive, 2013). There are multiple challenges facing this introduction, however, as “Net generation's access to immediate information has taught them to expect immediate answers and feedback. They often demand one-on-one attention from teachers and advisors because parents have been over-involved in their lives - often making all decisions. According to Gloeckler (2008), some parents are so involved in student lives that they send emails to faculty and administrators requesting information about their student's grades, schedules, student life, and extra-curricular activities.” (Worley, 2011), perhaps as an extension, these students also have had the

“helicopter parent” who kept them shut in from all of the “terrors of the outside world” due to incidents such as Columbine, Sept. 11, the Iraq War, and the increase in crime reporting in general (Elam, Stratton, & Gibson, 2007). This has led to a sheltered type of student, who may even check in with their parents about ten (10) times a week. However, there is evidence to show that it’s not necessarily the age or generation of the student, especially in collegiate settings, but the access to technology itself. In one of their books, a researcher claims that "Age may be less important than exposure to technology" (p.

125)(Provitera-McGlynn, 2007), resulting in the possibility of seeing someone far older who is much more tech-savvy than a Millennial or even “Generation Z” – the term coined for those who were born post-2002. Gen-Z has not really entered the college environment as of yet, but many of these middle- and high- school studies are gathering most of their 28 data (Montrieux, Vanderlinde, Schellens, & De Marez, 2015). While “Generation X” and

“Baby Boomers”’ data has been collected with these types of attitudes for years (see above). Facing these challenges of Millennial and Gen Z students while balancing the needs of the previous ones, strategizing, and overcoming them may indeed become the hallmark of a successful college professor in the coming years.

Even with a relatively recent study coming out of Korea (Han, K., Kim, I., Kim, R.,

Kim, H., Kim, D., Han, K, 2019), showing that – at least in their culture – a decline in faculties and learning with smartphones/tablets present, i.e. students used their phones on average 20 minutes out of total class time (a total of 28% of class-time), the attitudes, opinions, and perceptions were not recorded, coded, and reported. In a meta-analysis, it was found that there is much research lacking, and proper intervention design, pedagogical training for teachers, as well as training of instructors themselves through professional development is tantamount, and severely needed (Sung, Chang, & Liu,

2016a). In most of these reports, experiments, and findings, the attitudes of the students at the collegiate level have not been studied extensively, in order to get a more cohesive idea of how they themselves feel about integration of these devices.

It is the direction of this project to discover these attitudes, and if they do line up with the smattering of research already out there. Would students appreciate having access to their phones? Would it assist in their learning? Do they feel as if having it in their hands would constitute a positive learning environment or will they be far too

“tempted” to simply look up their Instagram/Facebook/Tumblr/Twitter/etc? Will their previous experiences cause more caution or enthusiasm? Will they feel burdened if an 29 older student or even a younger person who may not have been exposed to technology needed help? If an instructor actually used it for learning, would they continue using it for that purpose upon leaving the class?

Science Background Needed by Participants

This section will delve into the fundamental knowledge required by any subject who wishes to be a participant within this study. Earlier, it was detailed the amount of

Bioinformatics background that was put forth into this project, however those who participate will be students, and as such should have a foundation of basic Biological concepts going into the methodology employed.

Biology

Participants will need to be aware of some basic principles, concepts, and ideas that are core to the study and instruction of life sciences. Biology itself is a word that can be broken down into two smaller terms: Bio-, meaning life or living things, and –logy, a form of the medieval latin suffix –logie, which means “study of interest”. Hence, the term

Biology is defined as “The study of life”. Many introductory Biology lesson courses, specifically those at the school where the research will take place, begin with the

“Themes of Biology”, which goes into what life is, how it is made up, how it takes care of itself, reproduction, order, energy processing, homeostasis, and rudimentary evolution.

In addition, the lecture and lab course for this Introductory Course, where almost (if not all) of the data collection took place, has a heavy molecular biology component.

Molecular Biology is the study of the major molecules within a cell, focusing primarily 30 on the “Central Dogma”. The Central Dogma of Biology states that DNA makes RNA, which in turn makes Proteins. Proteins being the “workhorses” of any cell, responsible for types of work such as Mechanical, Transport, and Enzymatic functions. Mechanical is involved in repair, transport is what it reads like and is responsible for moving other molecules from one are to another of the cell (or outright leaving the cell), and enzymatic functions are responsible for the creation or destruction of other molecules.

Basic Chemistry

In order to understand how Molecular Biology works and its relation to Bioinformatics, one must have a grasp of basic Chemistry and how these terms such as atom, molecule, macromolecule, are defined. Biology is the top of a science “pyramid”, with Chemistry below it, Physics below that, and Mathematics at the foundation; each one builds and incorporates at least some elements of the other into itself as one moves “up” that pyramid. Since the Mathematics (statistics) will be detailed later in this review, and physics is not required for the understanding of what this project entails, this focus will be on Chemistry.

Matter is defined as something that has mass and takes up space. Matter and energy are the ‘building blocks” of the universe, and one cannot exist without the other. Matter can be large, such as a human, or very small, such as a microorganism (bacterium). Taking that further, matter can even go so small that it makes up things called Atoms.

Atoms are the smallest particles of matter with specific properties that cannot be broken down into other particles without losing those properties. Atoms are more commonly known by their element names such as Oxygen (O), Nitrogen (N), Hydrogen (H), Carbon 31

(C), Phosphorus (P), and Sulfur (S), and can be found on the Periodic Table of the

Elements. An element is another term for a specific atom that has specific properties.

Atoms are made up of the subatomic particles known as Protons, Neutrons, and

Electrons. Protons are positively charged particles that reside in the nucleus, or central area of an atom. Neutrons are not charged at all, and also reside within the nucleus; for reference, a nucleus in an atom can be analogized with the Sun at the center of the solar system, although instead of planets circling the sun in orbits, we have electrons. Electrons are negatively charged particles that orbit the nucleus in certain ways to help give that atom it’s properties. The number of protons, neutrons, and electrons give the element those properties, and then define what that element does in regards to itself as well as other elemental atoms.

Molecules are combinations of these atoms into greater structures. Atoms have a requirement called the “Octet Rule” for many of the more common occurring elements such as Nitrogen, Oxygen, Chlorine, Carbon, etc. The Octet Rule states that the outermost orbit of electrons’ amount must equal 8; if that outer orbit – or shell – does not contain 8 electrons, they then are able to form bonds with other elements to then gather those electrons to satisfy their needs. For example, an Oxygen atom has an atomic number of 8, meaning there are 8 protons, 8 electrons, and 8 neutrons in a stable atom (all charges are negating each other, ergo no net positive or negative total charge in the atom). Sorting out those electrons into orbitals, 2 always go in the first (this is true in any atom), up to 8 of the remaining number are then deposited into the second orbital.

Subtracting 2 from 8 gives us 6 net electrons remaining. This outer shell has only 6 electrons, and the rule states it needs 8 to be “happy”, so Oxygen can then form bonds 32 with other atoms to gain those two electrons to fill its outer shell. By bonding this way, these atoms then make up larger, more complex structures with different, newly emerged properties than the atoms themselves contain. Hence why one can combine Hydrogen and

Oxygen, both gases with distinct properties, into water, a liquid with a completely different set of properties. It’s also how one can combine Sodium, an explosive metal, with Chlorine, a noxious gas, and create table salt.

Macromolecules are when the smaller molecules (referred to as monomers) are then taken and connected together with other “like” molecules or non-like molecules to form even greater structures with different properties contained previously. For example, attaching many glucose, a short-term energy sugar, molecules together generate a macromolecule known as glycogen, which is a long-term storage macromolecule for cell energy. There are four (4) main categories of macromolecules in use within the life sciences: Nucleic Acids (DNA/RNA), Proteins, Lipids (Fatty Acids), and Carbohydrates

(Sugars).

Molecular Biology

As summarized above, Molecular Biology focuses on the three main molecules involved in the processes of cellular function. A cell is the smallest unit of known life, similar to the way an atom is the smallest known unit of essential matter. The cell contains a complex array of organelles, which are made up of multiple units of macromolecules, detailed earlier. For example, a common organelle is the Cell Membrane; this is the

‘wall’ that surrounds the components of the cell and as such the cell would not exist without it, it would simply be a loose collection of organelles floating freely in a liquid. 33

This cell membrane is comprised of hundreds of thousands of phospholipids, which are modified fatty acids, a group of macromolecules. Other organelles within the cell are made up of other various macromolecules and this process keeps building on each other.

Atom > Molecule > Organelle > Cell; this can keep going into Tissues (collections of similar cells), Organs (Heart), Organ Systems (Circulatory System), Organisms (Human), and so on until you reach the Biosphere (Earth) level. For this project, the knowledge required will merely be on the Molecular level.

DNA

Deoxyribonucleic Acid, or DNA as an acronym, is a macromolecule described and codified completely within the 20th century through the work of many researchers over time. DNA is known as the “Leader” in a cell, and is responsible for carrying the information on how to create and structure the various cellular components such as organelles, proteins, and others into a functioning whole. It does this due to containing units called “genes” within its sequence. DNA is a large macromolecule (around 3.6

Billion base pairs in humans) comprised of pairs of molecules known as nucleotides arranged in various sequences. Each monomer of DNA contains a sugar known as

Deoxyribose, a phosphate group (which is a Phosphorus atom bonded to four Oxygen atoms), and a Nitrogenous base (which is a single- or double-ringed structure containing various arrangements of Carbon, Oxygen, Hydrogen, and Nitrogen). These nitrogenous bases can be broken down into four (4) types: Adenine (A), Thymine (T), Cytosine (C), and Guanine (G). These can then be further classified according to their ring structure: 34

Purines (two-ringed) containing A & G, and Pyrimidines (single-ringed), containing C &

Figure 4.

Structure of a DNA Nucleotide.

(Credit: By Hbf878 - Own work, CC0, https://commons.wikimedia.org/w/index.php?curid=74023664 ) These nucleotides are bonded together through their Phosphate group joining with the sugar of the following nucleotide, and the nitrogenous bases facing inward bonded not by sharing electrons but through a strong attraction to each other (also known as a Hydrogen bond). This makes DNA a double-stranded structure that resembles a ladder; taking it one step further, this ladder is then twisted into a helical (or spiral) shape, which is why one may hear it referred to as a “double helix”.

Arrangement of these A’s, T’s, C’s, and G’s is what gives rise to the genes mentioned earlier. A gene is a certain sequence of DNA that can be read and transcribed into an 35

RNA molecule. This RNA is then responsible for the creation of the protein that is then used by the cell for whichever reason it is needed. In essence, it is the “cookbook” of the cell, containing all of the recipes any chef would need to make the dinner of their dreams.

RNA

Ribonucleic Acid, or RNA, is a copy of this DNA sequence and is used for various reasons, some regulatory within the cell, but the main function is to create a protein to be used elsewhere. In terms of structure, RNA is very similar to DNA, with the exceptions being: that it is only a single linear, globular, or hairpin strand, not a helix; it uses the nitrogenous base Uracil (U) in place of Thymine (T); and containing the sugar Ribose, instead of Deoxyribose. In all other ways it is similar to DNA; it is a macromolecule made up of nucleotides arranged in a certain way, bonded by their phosphate groups to the following sugars, and is important to the transport of these genetic codes to the other parts of the cell (or environment). If the DNA is the cookbook, the RNA is the single recipe desired for a dish.

Proteins

The RNA carries information to an organelle known as a ribosome within a cell, and the ribosome creates a protein out of the information presented. Proteins are the end result of joining multiple molecules/monomers known as amino acids into a chain, which then folds into a particular shape, and that shape is what gives a protein it’s function.

An amino acid has a fairly uniform structure, similar to the nucleotides above, and that structure is: a central Carbon atom, bonded to a carboxylic acid group (containing 36

COOH) on one end, an amine group (containing NH3) on the opposite, and a singular H on the third. The fourth bond – as Carbon can make up to four bonds – contains something called an “R-group”, which stands for “Rest of Molecule”, and that can be bonded to anything from a simple H to a large, complicated, double-ringed side molecule.

This is commonly referred to as a “side chain”, and is what gives each individual amino acid it’s distinct properties. There are twenty (20) amino acids, with different side chains, different properties, and different names.

Table 1.

The Twenty Amino Acids (a.a.), their abbreviations, and their letter representations.

Letter Letter Name Abbreviation Representation Name Abbreviation Representation

Leucine Leu (L) Aspartic Acid Asp (D)

Alanine Ala (A) Proline Pro (P)

Glycine Gly (G) Asparagine Asn (N)

Serine Ser (S) Glutamine Gln (Q)

Valine Val (V) Phenylalanine Phe (F)

Glutamic Acid Glu (E) Tyrosine Tyr (Y)

Lysine Lys (K) Methionine Met (M)

Isoleucine Ile (I) Histidine His (H)

Arginine Arg (R) Cysteine Cys (C)

Threonine Thr (T) Tryptophan Trp (W)

As with nucleotides in DNA and RNA, the sequence of these amino acids gives rise to the importance of the molecule, only in this case, the sequence determines the structure of these proteins. With proteins, structure determines function, and if the sequence is off (as in the case of mutation), the protein will not form the proper shape, and therefore not have proper (if any at all) function. The structure is determined by the interactions between the R-groups, some may have hysrogen bonding, some may have acid-base reactions, some are non-polar and will form certain loops to avoid their R-groups coming into contact with polar molecules such as water. Polarity is when one end of a molecule

(or macromolecule) has a net charge (e.g. positive) while the other ‘end’ has the opposite net charge (e.g. negative), so a non-polar molecule wishes to be away from all of that pushing-pulling.

Figure 5.

Structure of an Amino Acid.

(Credit: By Techguy78 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=89198282 38

Bioinformatics and Genomics As detailed previously within this manuscript, Bioinformatics is the “marriage” of life science and computer technology. Having the ability to sequence any piece of extracted

DNA/RNA/Protein with the machines available today requires the ability of researchers to analyze these multiple sequence data files and determine what this all means. That can be done through methods detailed above, and below, in addition to many that are not within the scope of this project.

Genomics is the study of these genes and their resultant RNAs, their sequences, how those sequences are arranged, and the overall impact on the cell and organism as a whole.

Proteomics is the study of these proteins, their structures, interactions, and predictions of such that can allow us to see deeper into the world of the cell, and therefore a deeper understanding of ourselves and the world around us.

Summary

These topics are what the workshop will ultimately deal with, and the participants will

(and should) be familiar with them prior to engaging with the material. As stated earlier, these topics are all presented within the scope of an Introductory Biology course, and can be tweaked to incorporate the knowledge presented here, should that not be the case in an outlier school, course, or program. The Bioinformatics/Genomics overview is given at the beginning of the workshop series, so those topics are introduced to the participants as fresh, new subjects and are detailed throughout the various parts of this manuscript, should they need to be created wholesale. For the purposes of this project, the workshop 39 will be given toward the end of the semester, wherein the students/participants have had ample time to learn these topics within their lecture course, or remember them if they take the class separately. This way the researcher/instructor is not required to go over every detail and bit of minutiae of background for the participants. Although the purpose of the workshops is to present the subject as isolated as possible, with the needed prior knowledge delivered as needed throughout the presentation, it is desired to have it available within this manuscript as well. Note: this was taken from the researcher’s own prior knowledge of the topics involved, and can be found within any Biology textbook.

Mobile Applications

This section will detail the fundamental design behind each application and why it should be included within the overall research question. Each one of these applications were thoroughly researched and cultivated to be used within the classroom as well as the laboratory should the need arise. In addition to these applications, there will also be a

“hub area”, wherein the four (4) sub-apps are all connected into a centralized main page, so the user can access the application they wish from the four splash buttons.

Application Design Review

For the prototype design of the main application, the design was kept simple, sweet, and easy to use. As this may be new material for many of the subjects within the study, the design of the application and sub-applications should be as easy to access as possible. By keeping it simple, the idea is to make it intuitive for the users without the need for a lengthy, in-depth tutorial.

The research into mobile applications began in the 1970s, with the emergence of 40 rudimentary home computers and how humans would interact with these machines. Since this started so long ago, it was not direct research into the mobile application arena, per se, but the foundations of the concepts were being laid that would be used throughout the years. As these early machines were large, bulky, and overall slow, the idea was to study what came to be known as Human-Computer Interaction (HCI) (Churchill, et al, 2013).

HCI involves the study and application of the interface between what is known as the

User – generally a human interacting with the technology – and the virtual world. In these original cases the virtual world was limited to massive mechanical keyboards and one- color monitors in either green or amber tones. In designing how these users would interface with the emerging technology, the User Interface (UI) was coined and developed for each new program being created at the time. A UI can be physical, in the case of keyboards and mice; it can be textual, as in the case of early DOS-based data entry and Linux programming; and it can be graphical, which is the basis behind the entire Windows operating system.

In order to generate an efficient HCI, some factors must be taken into account; potential users’ physical and cognitive capacities, sociological constructs and context, current computer science and engineering, and things such as graphic design capability. All of these factors, and others are of paramount importance. The first stage HCIs for those early, bulky systems were designed to allow easy access for a single user on a fixed location, i.e. the desktop computer (Kjeldskov, 2014). We find the second stage emerging in the early 1990s, as the internet reared up and showed its power for those who knew how to harness it. These 90s phones began using “features”, such as cameras, games, wallpapers, and personalized ringtones. Any flip-phone or other phone small enough to 41 fit in a user’s pocket – ala Nokia’s notorious brand of indestructible phones – are representative of this era. The “smartphone” time began somewhere around 2002, with the same capabilities as those previous feature phones, but they used the same operating systems, had larger screens, and a QWERTY keyboard and/or stylus for user input and early WiFi connectivity for access to the internet – the most famous example of this was the Blackberry line of phones and tools (Fling, 2009). The most recent era begins in 2007 when Apple introduced its iPhone, which was like smartphones of the past although it introduced a novel design for interaction with the phone; gone was the QWERTY keyboard altogether, with the entire screen representing the interface utilizing multi-touch capacities such as swiping, pinching, and the interactive keyboard that responds to a particular program’s needs. As can be seen, the needs of mobile design evolved from a physicality with size, shape, and analog input to a new dimension of total interactivity through digital means. This constituted changes to the overall design of the software needed to interact with these new behemoths in the industry. To adapt to the new environment, the software changed its label from “programs” to “applications”, with the short form “app” becoming the industry standard.

Development of Application Design

Identified were nine (9) major areas of app development, they are:

Page Composition. Page composition describes the overall layout and interface possibilities of an application’s sections. For example, the calculator application found on many phones of today is a “page”, and its composition is the button layout, how intuitive the interface is, and how users can interact with it. There was research into what type of 42 composition is more successful than others; as research also shows that there is not a

“one size fits all” to account for all different types of users. This research dove into menus, specifically, and how menus are used as a common delivery of the information within (Ling, C, Hwang, W, & Salvendy, G, 2007). Examined were the effects of 2D

(list) vs 3D (carousel, collapsible trees, revolving stages) menu display, structures as in depth of menu vs breadth of information displayed, adaptations, ordering, patterns, help areas, and individual differences. In these studies, 3D outclassed 2D menus, but 2D menus were able to contain much more information (Kim, 2011).

Factors such as culture and age were also considered in these studies, and were found that as far as culture goes, citizens from more Western civilizations preferred functionally grouped menu items, while those from more Eastern civilizations preferred more thematical groupings (Kim & Lee, 2007). In terms of age, it was found that the age of a user is merely a carrier of a larger factor: spatial ability. Older users were found to have a more difficult time with mobile technology, but it was also found that it was due to how they could not relate the new technology and interface to something with which they were familiar (Arning & Ziefle, 2009).

Display of Information. Mobile environments present a unique design issue, in that their screen sizes are very small (4”-8”) compared to a normal desktop environment (13”-70”).

Many of these desktop interfaces are in some list, drop-down menu, or other form, even in the Windows environment. A study found that visually appealing menus were more preferable to the traditional itemized, and the development of the “fish-eye” variant went forth. This was found to be very appealing, if not as efficient, and accepted by the community at large (Osman, et al, 2009). Through further experimentation, it was 43 discovered that grid-based menus were more preferable than list-based (Finley, 2013).

In addition, with the ability for users to actively use the touchscreen feature introduced in these newer-generation phones/tablets, the concept for making applications more suitable for immediate touch-sensitive interaction were created and tested. The more successful designs integrated graphical interfaces where pressing one section will display, execute a function, or change the page to one farther down the “list” within the app. Rather than selecting information from lists, the list was replaced with separate pages for each list item (Breuninger, Popova-Dlugosch, & Bengler, 2013, Quinn & Cockburn, 2009).

Control and Confirmation. This area deals with the users’ ability to control their ability to interact with the application, and not lose or commit to something in error. In such cases, something called control dialogue is displayed to the user to prevent these accidental interactions (Hoober & Berkman, 2011). A comparison would be the dialogue box that pops up in an operating system inquiring if you were sure about deleting this information/file. Unfortunately, there was not any research found on this topic regarding these types of regulatory inquiries.

Revealing More Information. There are two major types of processes for revealing more information within an application: full page and in context. Full page is exactly what it describes; it releases a lot of information in any given topic. In context displays the data or information within the context of the request; i.e. if a user inputs a positive number into the calculator, a positive number shall be displayed. Some types of revealing information include pop-ups, lists, and results returned from an inquiry. Again, very little 44 research has been done in this area since the introduction of the iPhone in 2007 (Hoober

& Berkman, 2011).

Lateral Access. Lateral access provides faster access to these categories of information stored within a mobile application. Two of the most common patterns are tabulation and pagination. Benefits of these are limiting the levels that users have to plow through in order to reach their desired information as well as returning constantly to a main page

(Hoober & Berkman, 2011). Once again, very little research has been done in this area as far as its effectiveness.

Navigation (or Links). Links are common across pretty much all platforms; most who have used a desktop environment has utilized a link at some point to get to information they were seeking (Hoober & Berkman, 2011). An example would be searching for “Star

Wars” on Google, and being presented with some results; the user would then click the link to the pertinent website with the information they sought.

Mobile navigation has some challenges, as how to display large amounts of information in a smaller environment, to compress the search results themselves, so a user can select from them, and the need for “visual” attraction. Mobile users want the bare minimum of interaction with linking to their desired results, so shortening the path is of utmost importance (Setlur, Rossoff, & Gooch, 2011). In this case, context-based-icons were designed as an almost industry standard (especially in the early years) for users to access their information easier. Example: a search result for a chess team would have an image of a knight pop-up for users to touch and continue their search.

When studying the navigation of large amounts of information, early studies focused on 45 gaze-tracing, but these required other devices attached to desktop-computers such as webcams (Yang, Mak, McCallum, Irani, Cao, & Izadi, 2010); this was accounted for by utilizing the new-gen phone’s front-facing camera, with some success (Cheng, Li, and

Feng, 2013), although it has not been pursued that much in recent years.

Buttons. Buttons have become the predominant method of interaction with mobile applications (Hoober & Berkman, 2011), at some point any user has had to press a geometrically-shaped area of the screen with their finger or thumb to access the next step in the application’s function. The ideal design for buttons was found to be the larger the better (Conradi, Busch, & Alexander, 2015), as when buttons are smaller, they cannot be pressed as well or as easily as when they are larger. Conrad’s study found that the optimal size for buttons were between 11x11mm and 14x14mm. Anything smaller and users were having difficulty pressing the correct one, any larger and they were having issues with the buttons taking up too much of the screen.

The issue with size of buttons comes down to the phone’s mobility; users will want to access their searches, data, and information while doing other activities such as walking, running, or having other applications running at the same time. Being free to use their apps during other activities increases users’ appreciation for and their willingness to return to these apps.

Icons. Icons are visual representations to provide users access to their target destination in a cursory manner (Hoober & Berkman, 2011). Usability of course is a large portion of the research into these particular aspects of mobile application development. Icon width- to-height ratios and the grid area of the icon itself was studied, along with the touchable 46 area of the icon, and the shape of the icon in users’ identifiability and preference (Im,

Kim, & Jung, 2015; Luo & Zhou, 2015). Overall, users preferred icons with a 4x5 grid area and a 0.9 width-to-height ratio, with an icon-to-background ratio related to screen size – users who had smaller phones were more prone to choose the larger icons, whereas users with larger screens (such as tablets), preferred smaller icons in order to fit more of them on a screen and have more variability and choice.

Aesthetics were another area looked into, with color being a main focus. Color combinations can cause a user to be more or less attracted to a developed application, and over the years 3306 color combinations were rated. 30 color combinations were consistently rated higher than others, with preference almost equal between male and female users, and colors can confer certain messages (Huang, 2012). It was even found that application status and color change (for example if there were an update) and users’ preference for continuing use if their favorite color combination were replaced (Ju, Jeong

& Suk, 2014).

Information Control. Controlling how users can view and access the information once it’s on their screens is an important area as well. With the limited size of these devices’ screens, and to account for the diversity of a user-base, providing the means to access this information can go a long way towards building goodwill with your users and recruit new ones. Zooming, scaling, sorting, filtering, and searching have been integrated into the touchscreen capabilities of almost all portable devices as of 2019. Although these have been brought into the usability of mobile devices, many users still preferred using links to access their information, after examining the zooming and scaling techniques across 19 devices (Garcia-Lopez, de-Marcos, Garcia-Cabot, Martines-Herraiz, 2015). Zooming also 47 relies on the gesture itself, and while the standard is to “pinch” one’s fingers open or close to zoom in or out, there may be newer manufacturers who may wish to use their own gesture, which could cause issues. Also, children, adolescents, and young adults are more likely to adapt to these gestures than older citizens (Hamza, 2014).

Searching is another control technique that must be adaptable for the user; in this respect, context plays a role in the search. For example, if a mobile phone user were to put into an application “Apple”, the search algorithm would/should return a list of Apple Computer resources, rather than the closes apple farm or facts about the fruit itself (Church &

Smyth, 2008). Gesture-based search was also proposed, and received positive feedback, but the integration into the larger industry has yet to be accepted (Li, 2010).

Sorting as well as filtering are techniques that allow the search results or control of information, as early website development limited results to 30-40 per page, with research showing that users did not look past the first 30, anyway (USDHHS, 2007).

Zhou, et al. researched precisely how many search results should be displayed on mobile devices and it supported that earlier research, in that users desired a limit of 30 per page

(Zhou, Sato, & Gao, 2007).

To summarize the process, putting many of the previous studies together brings us to this final section of the review. If users have access, can access, enjoy the access, and return for the access, they have to have a satisfactory mode to record their input and selection of their choices. Thus, the design of this application suite will integrate a simple touchscreen design, with buttons situated in the sweet-spot of 15x10mm rectangular shape, user input that is one-touch, search functions programmed to provide the most accurate results from 48 the data and input provided, and user interactivity with questions utilizing radio buttons for that haptic, tactile feel.

Science Governing Individual Applications and Sub-Applications

App 1: Test for Normality

As many standard statistical tests are based on the assumption of normality, it is almost always necessary to test whether an experimental dataset is normally distributed before performing statistical tests on it. Performing tests such as Student’s t-tests, ANOVA, or

Pearson’s Correlation Coefficient on non-normal data yields meaningless results (Blair &

Lawson, 1982; Calkins, 2017; Dunlap, Burke, & Greer, 1995; Hardin & Wilson, 2009).

By allowing researchers to perform a quick test of their data for normality on their mobile devices, they can then move forward, modify, or transform their data.

Graphically, a histogram is generated with the dataset to compare to the normal probability curve. This is the simplest test and can present the general shape of the data distribution. If the data do not fall under the bell-shaped normal curve, the dataset can be said to not have a normal distribution. A graphical test which gives a better representation of the distribution is the Quantile-Quantile (Q-Q) plot. This plots the standardized data against the normal distribution. The expected normal distribution is plotted against the observed distribution, and deviations are easily spotted (Aly & Öztürk, 1988). There are many tests to determine normality, from Pearson’s Chi-squared test, to the Anderson-

Darling test, to the Shapiro-Wilk test. Pearson’s Chi-Squared test uses sets of non-paired, categorical data to determine whether the differences in observations arose by chance.

Anderson-Darling tests whether a given set of data derives from a possible distribution; 49 assuming no parameters in its pure form, by adding in parameters (i.e. those of the normal distribution) (Anderson & Darling, 1952), it has been found to be one of the most powerful tools for such an analysis. Shapiro-Wilk performs an actual hypothesis test; the null hypothesis that the dataset comes from a normal distribution and rejection of the null signifies that the data are not normal (Shapiro and Wilk, 1965).

Figure 6.

The Normal Distribution and a Q-Q Plot in R.

After inputting the dataset, the user will see a graph similar to what is seen on the left, and will quickly note that the data are perfectly normally distributed. Similarly, the QQ plot pictured is one of a normal distribution, following an almost linear trend. As an example outside of the scope of this study, while in a lab course at the collegiate level, a collection of data can be obtained from a series of die rolls, with the students collecting and tabulating results of how many times each number comes up after N number of rolls. The students would write down a tally each result, and total at the end.

Utilizing the computer, either in the lab itself, a laptop brought from home, or the phone/tablet itself, the students will then recreate their tally table within a text file. After completing the file, it will be uploaded to Google Drive or directly to the App itself, and 50 then run through the tests for normality. Once they get their results, they can then see the distribution and answer an additional question sheet provided by the instructor.

App 2: Principal Component Analysis

Initially defined by the father of modern statistics Karl Pearson, principal component analysis (PCA) is a process that groups data points based on their principal component(s); hence the name (Pearson, 1901). The principal components are the features or variables we wish to examine most closely. For example, the Gross Domestic Product of a given country is based on a number of variables: The GDP of every fiscal quarter of the last 3-5 years, unemployment rates, inflation rates, the most recent Census data detailing how many citizens are in which industrial sector, and many others. PCA reduces the number of components to the ones that are most associated with the dependent variable. This is known as dimensionality reduction and can be performed in a multitude of ways that mainly fall into two categories: feature elimination and feature extraction.

Feature elimination excludes all features except a set of – for example – the “top 4” that would best predict the next year’s GDP. This streamlines the process, but it also does not give any information from the dropped variables.

Feature extraction is a combinatorial technique wherein the researcher uses all independent variables and then recombines them into new independent variables. The new independent variables are precisely coordinated (combinations of) from the old to be the best predictors. Through the process of combining several variables into new ones, dimensionality is reduced, and data will keep the “most valuable” features/variables. 51

Variance is then computed of these new variables (now called components), and it is determined which component has the highest amount of variance with all data points.

Variance is defined as the expectation of the squared deviation of the value from the mean of the dataset. Once the component is determined to have the highest degree of variance, this then becomes the Principal Component (Wold, Esbensen, & Geladi, 1987).

In a graphical representation, the grouped data points are then “collapsed” along a single line that represents the component of highest variance. An eigenvector is any nonzero vector based on a linear algebraic matrix, and only changes the scalar (or eigenvalue) whenever a linear transformation is applied to such, which in this case is the principal component.

Figure 7.

Visual Representation of how PCA Functions. (Credit: Laura Hamilton).

Not coincidentally, PCA using correlation also follows the normality assumption, as does most of classical statistics. Thus the PCA App will be a wonderful segue from the

Transform to Normality App (discussed above). 52

PCA is currently used in some online programs and websites such as Jalview

(Waterhouse, Procter, Martin, Clamp, & Barton, 2009), XLSTAT – which runs within

Microsoft Excel, and a site called ClustVis (Metsalu & Vilo, 2015). These sites and Java programs upload data (whether uploaded through a file or copy/pasted into the input fields) and deliver a graph that groups data via PCA. This helps the researcher see how data are clustered with respect to PCA.

For example, a biologist may be studying the numbers of two species of birds’ plumage colors based on gender. The components they wish to examine the closest are colors of the feathers, in this example blue and orange, but the second component could easily be sex (male vs female). This would then generate two conditions with, say, four replicates:

Orange males, Blue males, Orange Females, Blue females. Once the required observational period has passed, they will then gather their data points of each type and gender, transform into a normal distribution, and then run through a PCA to see if their data points are grouped in the desired patterns (i.e. all orange males should be grouped together). This is the first confirmation and will then allow the researcher to continue analysis more confidently.

Figure 8.

PCA displaying population data of two colors of birds, separated by color.

Males (blue), females (pink), are displayed according to plumage color and sex. The main reason for our choosing PCA as a starting application is that it is an established process, it’s easy to access for many scientists, and it can be performed on smaller datasets and files. It is a very versatile tool and subject-agnostic. One of the major problems faced with generating analytical tools for the mobile environment is that many researchers want high-powered computing for massive datasets (terabytes of data), which the hardware of mobile devices cannot fully support – yet. Thus, allowing the program to run with a smaller set, and will not eat up the memory and processing power of the phone/tablet, causing a malfunction. While that is an advantage of integrating PCA within this App suite at the current time, the increase in hardware ability over the next few years will allow larger, more versatile datasets to be analyzed, managed, and 54 displayed; when that occurs, this program will already be established and therefore a more viable research tool for the researcher.

Another reason for the choice was that this process is available within many programming languages, given that this is a mathematical process. Almost every line of code is involved in some sort of numerical operation to determine what it’s going to do next. For example, one could have an if-else loop, and whether the function stays within the loop is determined by a numerical value or operation. Once the loop reaches the pre- determined number for exit, the program then outputs the final readout, whether that is a print statement or an actual numerical value. This makes PCA ideal for integration, as it can be done within Java with relative ease.

App 3: Probability Behind Sequence Similarity

Probability is another central tenet of statistics - a measure of how likely an even is to occur within a given sample space. A common example is rolling a six-sided die, or flipping a coin, and the chance that the die rolls on an even number greater than 2, or the coin landing on “heads”; in those cases, the probability is 1/3 and 1/2, respectively. The higher the probability, the likelier an event occurs. The binomial distribution is a discrete probability distribution which measures a Boolean-type outcome, such as success/fail, yes/no, heads/tails, true/false, one/zero.

It is surprising to find how many undergraduates and graduate students in the biological sciences are not familiar with the fundamental concepts behind sequence similarity calculations [personal observations in the undergraduate classroom]. Sequence similarity is based on simple probability concepts and empirical experimental observations first 55 recorded by Margaret Dayhoff and then others. Dayhoff’s observations led to the PAM matrices, which present frequencies of point mutations between each of the 20 residues based on the observations of 1,572 changes in 71 protein families (Pevsner, 2009)The

PAM matrices show, for example, that C and W are much, much less likely to mutate than any other residue. Dayhoff’s experiments also led to a general knowledge of residue occurrence rates, which are far from equal (Pevsner, 2009). Without knowing this, determining the difference between two sequences would be restricted to using the binomial theorem.

For example, to determine the probability that a sequence of length 20 has two M’s and having no knowledge of residue mutation nor occurrence rates, the problem would be solved as follows, using the assumption that all residues occur at equal rates, and the binomial formula:

Figure 9.

The Binomial Formula.

2 18 Then 푃(푋 = 2) = (20) ( 1 ) (18) = 7.1% 2 20 20 56

Using empirical occurrence rates for just the residue M would alter the probability of success from 5% to 1.5% and change the result just for this residue drastically. Similarly, to determine the probability of any randomly chosen sequence from a database of size S to contain a residue M, the students can choose to use the prior biological knowledge

(PAM, BLOSUM, or other scoring matrices) (Dayhoff & Schwartz, 1978; Henikoff &

Henikoff, 1992) for a more meaningful result, or they can simply apply the binomial theorem for a “quick-and-dirty” approach. The same is true for pairwise sequence alignments. A very simple (and standard) sequence similarity score can be computed by counting the number of identical residues between the two sequences and that number by the length of the longest sequence. A more biologically meaningful similarity score can be generated when mutation rates are incorporated. That is, if sequence GPAV is compared to GPDV, the simple score is ¾ whereas the score taking mutation rates into consideration would give the mutation AD a non-zero weight.

This application will familiarize the user with nucleotide and protein sequences, the probability of their occurrences, the probabilities of specific mutations and perform simple pairwise sequence similarity calculations. The application will also show the user easily that PAM and BLOSUM matrices are simply probabilities that were calculated from real experiments in the 20th century that show true observed mutations between two residues. The application will also force the student to become familiar with a few standard sequence databases (BLAST, EMBL, UniProt, PDB), and more specialized databases (XenBase, RGD, WormBase, SGD, and TAIR) (Bowes et al., 2007; Chen et al., 2005; Cherry et al., 1998; Shimoyama et al., 2015; Swarbreck et al., 2008). 57

Specifically, the Probability Behind Sequence Similarity App will have two sections. The first will allow the user to input a short-ish sequence (length up to L=25) and will calculate the probability of this sequence’s occurrence without any prior biological knowledge, under the assumption that all bases or residues are equally likely. For protein sequences, the App will then calculate the probability of the sequence occurring under the basis of standard residue occurrence rates. The point here is to see notable differences in these two calculations. The second section will allow the user to input two sequences of short-ish length (again, up to 25 base pairs or residues); the App will compute the sequence similarity score in a basic way presented above, as well as with a chosen PAM or BLOSUM matrix, or the analogue for nucleotide sequences (e.g. EDNAMAT,

EDNAFULL). Again, the point is to show the differences between informed alignments and those performed without prior knowledge.

In addition, the Probability Behind Sequence Similarity application will also be incorporating the NCBI/NIH’s BLAST algorithm. This is one of the oldest

Bioinformatics analysis tools that is widely available for free usage from the NCBI’s website. BLAST stands for Basic Local Alignment Search Tool and was designed by

Stephen Altschul, Webb Miller, Warren Gish, Eugene Myers and David Lipman in 1990.

What this tool does is take a sequence of DNA that the user inputs – called a query sequence - and then compares it to the millions of stored genetic sequences in the NCBI’s database. It then will return from one to a few dozen likely results based on the alignment score assigned to each sequence as compared to the query sequence. As each nucleotide or amino acid are compared to each other, a match is assigned a positive score (usually a

1), a mismatch is assigned a zero, and the introduction of a gap in the comparison to 58 make a ‘better’ alignment is assigned a negative value – with the extension of that gap extending that negative value. The higher the score, the more likely the sequence similarity (Altschul, Gish, Miller, Myers, & Lipman, 1990).

Figure 10.

NCBI BLAST Main Page.

The central interface of the desktop BLAST website. Users can access the Nucleotide, Protein, or translational databased from here. (Source: NCBI) It does this by using a local alignment (hence it’s appearance in the name), rather than a global alignment. A global alignment will compare the entire sequence of the query and the possible matches at the same time, going through the entire set of sequences one nucleotide or amino acid at a time. This is incredibly time and computing power consuming, with the results being more accurate, however the modern research may not 59 have the time to spare in order to get these specifically accurate results. Instead, BLAST uses a local “word method” alignment. What this does is break up the query sequence into a series of eleven (11) letter words of nucleotides or three to six (3-6) letter amino acid groups, and then compares these shortened forms of the genomic query sequence against every possible match in the database. It then uses these word matches as a guide to assign the scores for similarity assignment. The more matches a set of sequences have, the higher score they receive, and the more similar they are overall (Altschul et al., 1990).

These scores are also evaluated utilizing a value that is expected when the sequences are compared. This value is calculated using the scores from the High-Scoring Pairs (HSPs) being put through a Poisson-like distribution analysis. These HSPs are put through a series of algorithmic evaluations, looking for the log of the values when used with the lambda and kappa assignments (λ = 0.318, Κ = 0.13, respectively, when there are no gaps introduced in a given sequence comparison; introducing gaps will of course alter those values) and their average “H” to produce a simple Expected value (E-Value). This E-

Value can be used to evaluate how similar the sequences are without the introduction of random chance into the mix (Mount, 2004). An E-Value which is close to zero (0.0) is considered valid, and the similarities are not due to a random mutation or a glitch into the system; it delineates that the comparison can be used. E-Values can be considered similar to P-Values in this regard; although one is used with hypothesis testing in statistical analyses, and the other is used as a confirmation of the test run within the in silico environment (Altschul & Gish, 1996; Mount, 2004).

Figure 11.

BLAST Interface and Results.

Blast interface, seen on the left, is where data is input either in FASTA or accession number format. Results can be displayed in the form on the right, where comparisons to homologous genes are located and shown for the researcher’s analysis. (Source: NCBI)

App 4: Simple CRISPR Analysis

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) is a recent technique discovered to assist in the field of gene editing. As this technique’s understanding and application is advancing at a rapid pace, the ability to understand just what it is and how it functions is important, to researchers as well as the undergraduate student and public in general. This holds the potential to change the world in a short amount of time, ergo the advancement of understanding should be just as rapid, to avoid error in research, public policy, and the consumerism spheres.

CRISPR is a gene editing tool that comes from bacteria (Mojica, Dıéz-Villasenõr, Garcıá-

Martıez, & Soria, 2005). It is an enzyme (usually Cas9) guided by a specialized RNA to a gene of interest that then cleaves the gene. Bacteria use this as a sort of immune system; 61 by carving up viral DNA and then incorporating it into their own genome, it creates a

"memory" of the virus that is used as a recognition of a subsequent infection. It transcribes what is called a guide RNA (gRNA or crRNA - for CRISPR) of usual 20 base pair length, which then through recognition of a 3-base pair series at the edge of the RNA called the PAM sequence (Protospacer Adjacent Motif), recruits the Cas protein. Each organism's specific Cas recognizes a specific PAM, the most popularly used is from Streptococcus pyogenes (Ran, Cong, Yan, Scott, Gootenberg, Kriz, Zhang, 2015) and it recognizes a motif of NGG, where N is any nucleotide and the others are Guanine.

The entire complex is then guided to the gene of interest - in this case a viral invader - where through complementarity, the RNA 'attaches' to the DNA, causing the Cas protein to be in the position it needs to be. At this point, the two cleavage sites (RuvC and HNH) cleave the DNA, one strand in each site, thereby rendering the invader moot.

Figure 12.

CRISPR Overview.

The CRISPR RNA is complementary to the Gene of Interest (GOI) and recruits the Cas9 Protein to cleave the DNA at the RuvC and HNH locations. (Course: Integrated DNA Technologies, inc) 62

For editing, the same principle is used, although this generates a double-stranded blunt- end break, and as is the case with many blunt-end cleavages, the repair mechanisms will invariably introduce a mutation through "cutting off" the nucleotides at the edge of the break, so this can either shorten or lengthen the sequence, or render a mutation moot by allowing the DNA repair cycle to replace a bad nucleotide with a proper one; a lab in

China accomplished this in 2013 with cataracts in mice, restoring the gene and eradicating the defect for at least three generations (Y. Wu et al., 2013). One can also introduce a gene to be spliced into the genome by adding that slice of DNA with the

CRISPR-Cas9 complex, and with a little bit of luck that the complex does not carve up your introduced gene, the CRISPR will cut the DNA, and the cell's mechanisms will then incorporate that new strand in through Homology-Directed Repair (Barrangou &

Doudna, 2016; Doudna & Charpentier, 2014; Sander & Joung, 2014).

There is also the possibility to just 'deactivate' the Cas protein, making the cleavage sites useless (known as dCas). The reason to do this would be to for the gRNA to bring the

Cas protein to the GOI and then simply have it 'sit' there. This can allow a down- regulation of that GOI and produce a knock-out-like environment without ever having to lose the gene completely. The Cas just blocks any other proteins from binding, therefore zero transcription occurs. One can also attach a fluorescent protein to the deactivated Cas and then have it light up to show the location of the gene on the actual strand. Another technique is to attach an up- or down-regulating effector molecule to the dCas which then can activate an adjacent gene (Barrangou & Doudna, 2016; Sander & Joung, 2014).

Figure 13.

CRISPR/Cas9 Activity.

Upon recruiting the Cas9 Enzyme, the complex can do any of the above functions with the GOI; a) Cleavage of the gene causing an indel mutation, b) Cleavage causing a replacement, utilizing a donor piece of DNA as template, c) Two complexes cleave and remove a large section of DNA, which is then rearranged, d) Cas9 can be deactivated, and have an activator attached to activate a neighboring GOI, e) Cas9 is deactivated, with an effector attached that modifies the DNA near the bond area, f) Cas9 is deactivated, with a fluorescent marker attached to “tag” GOI. (Credit: Sander & Joung, 2014) With the recent development revolving around the birth of the “CRISPR Babies” in

China, the need for education in this topic is increasing almost daily. Gene editing opens up multiple possibilities and as such, could cause radical change within the sphere of scientific research. Whenever a new, revolutionary technique such as this comes on to the 64 scene, it has a tendency to generate a lot of fear within the general community, and the researcher has already encountered students asking about “Designer babies” that only the

“rich could afford”, setting the standards for success in the future higher than most, and separating the “haves” from the “have-nots”. In addition, the pioneers mentioned and cited within this review, Jennifer Doudna and Emmanuelle Charpentier, won the Nobel

Prize in Chemistry for the year 2020. This has pushed CRISPR back into the spotlight, and the need for education within this area has increased.

Learning Theories

As this will be applied within the collegiate classroom, the basic learning theories must be examined, evaluated, and applied where necessary. There are multiple types of learning theory, developed over years of research, collaboration, incorporation, and extensive application. The three “main” learning theories are Behaviorism, Cognitivism, and Constructivism; each one approaches student learning from a different angle.

Types of Learning Theories

Behaviorism is founded on the idea that the learner is passive, responding to environmental stimuli and not an active participant in the process. This was brought forth by proponents such as Ivan Pavlov and his very famous dog-and-bell-ringing experiment,

B.F. Skinner and the mouse in a maze experimentation, and others such as John Watson and E.L. Thorndike. In all cases, the idea of reinforcement, whether positive or negative, is posited as the impetus for student learning. This is still used in many types of training modes, as those used with animals such as dogs and cetaceans, and more commonly referred to Operant Conditioning. Humans, specifically, are seen as animals that respond 65 to their environment almost exclusively; an example would be a factory worker that has a set of stimuli and rewards given over their career; either take away the stimuli or change the reward, and that worker will modify their behavior accordingly. The worker would not process the changes, create their own hypotheses, and perform their own tasks to figure out what to do next; they would simply change their behavior and move forward.

A classroom set up in a behaviorist manner would also follow a similar concept, and the severe challenges involved to keep these stimuli and reward systems up would be exhausting to say the least, for even the most seasoned instructor.

Cognitivism is a learning theory that puts forth the idea that learners are not simply instinctual creates that respond to external stimuli and require reinforcement to process and retain information about the outside world. What this theory posits is that learners – mainly humans in this case – are more like computers, wherein we have information given to us, we process it, and we then create outcomes based on that internal processing.

Overall the learner is viewed as an information processor and champions the idea of thinking before acting. Rewards may be used, but for the most part, we are dealing with the upper echelons of our thinking brains, rather than the “lizard” portions that deal with a fight-or-flight style of learning. An important feature of this theory is that an educational foundation must be present before instruction can be considered effective, and this idea is called a “schema”; a schema is used to help connect any previous knowledge, ideas, and concepts with the learner’s new experiences, information, and/or knowledge. By guiding the learner to their destination and connecting it to previously scaffolded information the learner makes a more informed judgment. A classroom set up in this manner is typical of what can be found in most classrooms, even today: a lecture- 66 focused delivery system of information where the learners listen, process, and report results through formative and summative assessments.

Constructivism is the theory that puts forth the notion that the learner is an active part of their process. They are not fed stimuli and their responses are observed (or retained in the learner’s case), they are not given a schema and a structured instructional glut of information to process and deliver results, they are instead guiding their own learning with the goal of eventually developing their own questions and forming the means to answer them. This was in response to Cognitivism – and is considered as simply a branch of the other theory, rather than its own – and that it posited the learner succeeds when they are just fed information. An instructor may give a student-learner a goal or a general over-arching question, and the learner would then be responsible for guiding their responses to answer the question at hand with their own toolkit. In essence, humans create an individual meaning out of their learning, experiences, and knowledge rather than acquiring it from others. Proponents and creators of this theory were the likes of

John Bruner, Jean Piaget, Lev Vygotsky, and John Dewey. Cognitivism sees the human brain as a “black box” which must be opened to fully achieve the task(s) at hand,

Constructivism sees the human brain as a sponge that absorbs everything around it and forms its own processes in its own time to formulate the unique approaches to deal with these questions, issues, concerns, or outward stimuli. A constructivist classroom is what more institutions are moving towards; wherein the students/learners are given a bit of background information for understanding, the instructor then has basic guidelines for what is expected, and the students are then given activities or open-ended avenues to construct their own results. Another widely used term for this is inquiry-based learning, 67 where the learner is directing their own development through asking their individual questions. It is nice how it all wraps back around to the original concept.

There are a couple of smaller, less well defined and lesser used Learning Theories:

Humanism and Connectivism. Recent advances in psychological research as well as technological leaps with respect to how humanity interfaces with information have generated these new, budding theories. Humanism is the idea that learning is used to help an individual achieve their personal potential, through activities that guide the learner to self-actualization; it is much more focused on experiences, opinions, and dignity/freedom of humans. It pushes them to find their own limits and does not generally allow an evaluative gradation of any kind; that any type of evaluation by another is diminishing the learner as a whole. A Humanist class would be similar to what is seen in the

Montessori Private School system, wherein students are their own person and there is little, if any, structure provided; every idea the student/learner has is championed and only the student can determine if they have reached their max potential in a certain area.

Connectivism is a fairly new idea that came about in the mid-2000s and posits that the learner is enhanced through interaction with the rising wave of digital information and the tools being created to interact and interface with said networks. With the creation of the internet and its rapid acceptance by the public at large in the 1990s, tools such as desktop computers, laptop computers, cellular telephones (later smartphones), palm pilots, personal tablets, and now wristwatches were developed to allow humans to interact with information in ways that could only be dreams of in previous generations.

Gone are the days of poring through tomes to find one sentence that backs up an argument, or cursing the gods when the resource you are looking for has been borrowed 68 by another and one must wait the time allotted for its return. Anyone with the rudimentary knowledge of how to use the internet can browse its seemingly endless valley of resources, and that allows students and learners to connect with the knowledge in ways previously unknown or unable to be studied in the past. A Connectivist classroom is still not defined well at all, but that is the hopes and goals of this research project to determine. By granting students access to more technology and using the hardware to interact with software in various ways, student learning may be changed in a positive way.

Constructivism

After examining the various “Big Three” learning theories, it was determined that this type of project would fall under the Constructivist umbrella much more than the others, and as such, the researcher decided that this would form the backbone of the theoretical approach behind the project. By examining Constructivist views, ideologies, and applications, it can be shown that this would be the proper avenue to take with regards to introducing a new intervention into a Community College Introductory classroom environment.

As stated above, Constructivism was generated as a reproach to Cognitivism, in such that

Cognitivism saw learners as stationary objects that must be fed information to process, open up, and report results. Many arguments were made that the Great Ancient Greek and

Roman Thinkers were proponents for the Cognitive method, in that notable figures such as Plato, Aristotle, Sokrates, and their contemporaries sat around and lectured each other endlessly. This was continued throughout the years, most prevalently in the university 69 educational methods, where the experts in particular topics held massive lecture sessions where they would drone on and on about their knowledge and students had to keep up through rote processing of said information.

Constructivists argued that children learn from experiential interactions with phenomenon. This results in taking previous experience and combining it with the new learning experience at hand to assimilate or accommodate new ideas (Piaget). More contemporary constructivists add more social experiences to the learning; through experiences and interactions with the outside world, their peer groups, and their parental units (Vygotskian influence as stated in the next section). Student learners can learn through rote listening of lectures, but it may not be the most efficient means of conveying information to a budding young mind. Elliot describes Constructivism as, “an approach to learning that holds that people actively construct or make their own knowledge and that reality is determined by the experiences of the learner. (Elliott et al., 2000:256)” and this is about as succinct as it can be made. Arends (1998) elaborates a bit on this by saying that learning is a personal construction of the student learner through personal experiences and events, and that the meaning derived is an intersection of previous knowledge and these new events.

Principles of Constructivism

There are five over-arching principles of this Learning Theory, that appear multiple times in the literature. These tenets are central to the application of this theory within any classroom setting.

Knowledge is Constructed, rather than innately known or passively taken in 70

This is how Constructivism breaks with Behaviorism (innate) and Cognitivism (passive).

Phillips states:

“Constructivism's central idea is that human learning is constructed, that learners build new knowledge upon the foundation of previous learning. This prior knowledge influences what new or modified knowledge an individual will construct from new learning experiences (Phillips, 1995)”.

So it builds a little on the previous two theories, in that humans must possess a certain amount of pre-existing knowledge about the topic through innate reactions (i.e. a person knowing a flame is harmful through touching it at one point and reacting accordingly) and passive learning from another (i.e. when a lecturer gives an hour’s worth of information on Cellular Respiration). It then takes that and builds that the innate and previous knowledge is not enough, that the learner requires outside interactions and experiences to generate new knowledge.

Learning is an Active Process

This ties into the previous point, in that learners must be engaged in their own learning actively, and not passively. Science classrooms and laboratory settings are ideal for this type of learning, in that they actively push the learner to engage thoughtfully with the world around them.

Knowledge is Socially Constructed

Learning is not something that is done in a vacuum, or through someone telling you how the world works. It is an active process that is assisted by interactions with those around you. It is something we do together, not an abstract idea (Dewey, 1938). Vygotsky wrote 71 that the environment in which a student learner grows up influences what they learn and how they perceive things. The community plays the key role in creating meaning for the learner, according to his postulate, and that cognitive development is guided by instructors and the peers of a learner through their individual Zome of Proximal

Development (ZPD). The ZPD is the area where a student learner is close to mastering a new skill, lesson, or experience, has some prior knowledge of the topic at hand, and is assisted to mastering the skill through guidance. Vygotsky stated it explicitly as:

"the distance between the actual developmental level as determined by independent problem solving and the level of potential development as determined through problem-solving under adult guidance, or in collaboration with more capable peers" (Vygotsky, 1978, p. 86)

Hence, learners interact with that knowledge, their peers/instructors, and fins the answers or skills to achieve their own results.

All Knowledge is Personal to the Learner

As the learner is the prime active pursuer of their understanding and knowledge discovered, this leads to the notion that the achievements earned are personal, and therefore carries more import. This also means that each student in a given group will learn differently, and may not walk away with the same result or interpretation of the data/information given.

This contradicts the previous point somewhat that learning is constructed through social interaction. Fox (2001, pg.30) puts forth the suggestion: 72

(a) “that although individuals have their own personal history of learning, nevertheless they can share in common knowledge, and (b) that although education is a social process, powerfully influenced by cultural factors, nevertheless cultures are made up of sub- cultures, even to the point of being composed of sub-cultures of one. Cultures and their knowledge-base are constantly in a process of change and the knowledge stored by individuals is not a rigid copy of some socially constructed template. In learning a culture, each child changes that culture.”

So the learner may construct their individualized informational database, but they also can return the favor by influencing the learning of those around them. Each of these ideas carries weight and should be considered when constructing a curriculum or lesson plan.

Learning Exists Completely in the Mind

Constructivism states that knowledge exists completely within the human mind, and does not have to match up with the “real world” (Driscoll, 2000). Learners will be consistently attempting to develop their own views of reality based on their constructed knowledge, and as such will be changing their perceptions in response to these new developments.

This is most famously echoed by the television show Mythbusters’ host and participant

Adam Savage, “I reject your reality and substitute one of my own (2008)”, which was also found within a 1984 film named The Dungeonmaster. While this is a facetious take, it is essentially what learners do regardless of whichever theory is used in their process;

Behaviorists alter their behavior and therefore perception based on their experiences,

Cognitivists alter their teaching approaches when the students are not interested or failing their examinations, and Constructivists alter their perceptions based on the information collected and how it’s constructed by interactions. 73

These basic principles are what Constructivists use in the development of their classroom lessons, plans, and examinations. Or, at least, they should be if effective delivery of the

Learning Theory is to be expected.

Constructivist Classroom

So how would a Constructivist Classroom be set up and/or look to an outside observer?

As this theory promotes the idea that the student learner is in control of their processes, the teacher then is perceived – and should be relegated – to a role of facilitator. In

Constructivist classrooms, the instructor merely provides the framework and/or scaffolding for the path(s) the students will take in reaching their academic goals, and is there to assist should the student learner(s) have questions or require assistance during their exploratory phases. The instructor must be made aware of students’ pre-existing knowledge base so they can effectively create these schema and scaffolds for their pupils’ learning tracts (Oliver, 2000). Scaffolding can include such activities as modeling a skill, dropping hints or clues during the activity process, and adapting the materials delivered or activity presented; this can be done “on the fly” during the instructional period or through formative assessment by said instructor through observations and the amount of student issues for later implementation (Copple & Bredekamp, 2009).

Tam (2000) posits some key points that are tantamount to having a functioning constructivist environment.

 Knowledge will be shared with Teachers and Students.

 Teachers and students will share authority.

 The teacher’s role is one of facilitator or guide 74

 Learning groups will consist of small numbers of heterogeneous students.

In addition, these key concepts are presented as a compare/contrast to how a

Constructivist classroom is set up opposed to a more traditional setup.

Traditional Classroom Constructivist Classroom

Strict adherence to a fixed curriculum is Pursuit of student questions and interests is highly valued. valued.

Learning is interactive, building on what the Learning is based on repetition. student already knows.

Teacher-centered. Student-centered.

Teachers disseminate information to Teachers have a dialogue with students, students; students are recipients of helping students construct their own knowledge (passive learning). knowledge (active learning).

Teacher's role is directive, rooted in Teacher's role is interactive, rooted in authority. negotiation.

Students work primarily alone Students work primarily in groups

(competitive). (cooperative).

(Tam, 2000)

As can be seen, the Constructivist approach is to enhance student interaction, and encourage student-centered learning, through activities and opportunities presented by the instructor and/or learning district or school. Creating a collaborative learning environment is essential in generating student interest, according to this Learning Theory. 75

Honebein (1996) described seven pedagogical questions and ideals that the Constructivist classroom should be designed to address;

 To provide experience with the knowledge construction process (students

determine how they will learn).

 To provide experience in and appreciation for multiple perspectives (evaluation of

alternative solutions).

 To embed learning in realistic contexts (authentic tasks).

 To encourage ownership and a voice in the learning process (student centered

learning).

 To embed learning in social experience (collaboration).

 To encourage the use of multiple modes of representation, (video, audio text, etc.)

 To encourage awareness of the knowledge construction process (reflection,

metacognition).

Pedagogy is another term used for Learning Theory, i.e. Behaviorism, Cognitivism,

Constructivism, etc. are all pedagogies, and these terms can be interchangeable. These key points illustrated above are important to address and incorporate into a proper

Constructivist classroom. One can see that all of these research discoveries and descriptions are all pointing toward a similar goal: Students direct their own learning. The strengths of this theory is that it allows students to direct their own learning, as stated multiple times, and to have autonomy over their ownership of both learning and assessment. Content can also be delivered using multiple avenues and approaches to cater to students’ specific needs. On the flip side, some limitations are that examinations 76 cannot be given to address each individual’s learning ability, style, and progress, so the instructor may need to get extra creative in their evaluations. Also, with its distinct lack of structure, there may be some students who will struggle extensively; as many children

(and most adults) are raised with rigid or at the very least a loose structure and/or scaffold of rational steps to take in the completion of any task.

Connectivism

Connectivism is a relatively new Learning Theory that emerged in 2005, described by

George Siemens originally as an alternative to the “Big Three” in that none of them addressed the booming digital age of the internet and its accompanying hardware to harness (Alger, 2005). It acknowledges that learning is no longer an individual experience – contrasting with Constructivism – but is connected to others through their sharing of digital information and the ease of the hardware associated with such.

Through these interactions such as Communities of Practice, work-related tasks, and personal networking, it addresses that humans are social animals, and as such folds in the informal learning made through these connections with people and technology into the student’s more traditional, formal learning. The main thrust of the theory is to promote prevention of information overload through sharing and forming connections through these sort of ecologies, communities and networks; it’s facilitating connections while encouraging a love of life-long learning among the participants (Siemens, 2005).

Siemens (2005) describes eight (8) core postulates of Connectivism: 77

 Learning as well as knowledge is diverse, drawing from a broad range of

opinions.

 Learning is a slow, continual process of ‘connecting’ to individual ‘nodes’,

similar to a network.

 Learning could ‘live’ in technology, or non-organic (human) arenas.

 Capacity and ability to increase learning and retain that knowledge is more

pertinent than the current body of knowledge.

 Nurturing and maintaining these outside connections is paramount to facilitate a

love of lifelong learning.

 Ability to see these connections between ideas, concepts, and subject matter is a

skill shared at the core of all learning.

 The relevance and recent (current) developments of these learning tenets is at the

heart of Connectivist ideals.

 Decision-making is in itself a learning process. Deciding what to learn and the

relevance of the information does not necessarily mean that the information will

be correct even as soon as the following day. The shifting landscape of the

information, and the speed with which it can be uploaded/changed in the rapidly

evolving environment allows for this possibility.

Ertmer and Newby (1993) developed a five-question framework to elucidate and elaborate on what exactly defines a learning theory, and their questions can be applied to the Connectivism theory itself: 78

 How does learning occur? Learning is distributed within a social and

technologically enhanced network. Learning takes place through the recognition

and interpretation of patterns.

 What are the influencing factors? The diversity of the network and strength of

the ties within the network are among the most influential factors.

 What is the role of memory? Memory is used in identifying adaptive patterns. It

is representative of the current state that exists in networks.

 How does transfer occur? Transfer occurs by connecting to or adding nodes

 What types of learning are best explained by this theory? This theory can best

explain complex learning, a rapid changing core, and the ability to incorporate

diverse knowledge sources. (Ertmer & Newby, 1993)

There are two additional questions that can be applied: “What basic assumptions/principles of this theory are relevant to instructional design?” and “How should instruction be structured to facilitate learning?” (Ertmer & Newby, 1993), but for the purposes of this project, these questions will be answered within the methods developed and presented.

As this is a new theory, it has not been accepted fully as such; it is still thought of primarily as a pedagogical framework and not actual, fully developed Theory. There is also little research to be found for its actual implementation in the classroom environment, refer to the earlier section in this chapter detailing technology’s integration into the learning area for explanations of why this may be. 79

Connectivist Classroom

As the researcher discussed earlier, so shall this project take a look at what a Connectivist

Classroom looks like, how it is set up, and what the goals of the classroom learning will entail. The previously described and explored theories suggest that learning takes place entirely (or almost entirely) within the head of the individual. Connectivism suggests that in our modern era, that is next to impossible to accomplish. Siemens argues that the world of information is just far too complex to process learning completely in the mind and “we need to rely on a network of people (and, increasingly technology) to store, access, and retrieve knowledge and motivate its use” (Siemens, 2006). As the world of information continues its inexorable evolution, so too will the learner’s capacity to obtain, retain, and adapt their own stored databank. The facts’ validity and accuracy will also change over time due to the discoveries made and the contributions to the shared data structures, and as such the new learning paradigm must be as adaptable as the information itself, or else the learner will not be able to understand, store, and retrieve the information required to function properly. Note: the researcher has adapted the terminology to reflect how human learners and information storage and retrieval systems are becoming more alike every moment.

The role of the internet is a sort of double-edged sword; it allows the users unprecedented access to the collected informational sources of those who have contributed to it over the years, however the ability to discern what is truly valuable data from the secondary chaff and extraneous bits, which may end up serving the reverse purpose of informing the user.

In this venture, the role of the educator and instructor is to become someone who 80

“create[s] learning ecologies, shape communities, and release learners into the environment” (Siemens, 2003). There are three main courses of thought in what a role of an instructor in a connectivist environment would be:

 Educator as a Master Artist (Seely Brown, 2006):

 Students create work which is in full view of peers.

 Educator can observe activities of all students and draw attention to specific

approaches.

 Students learn from each other and from suggestions offered by Master Artist.

 Educator as a Concierge (Bonk, 2007):

 Educator directs learners to resources and learning opportunities

 Educators have quick access to resources that can be shared with learners

 Employs a learner designed program of study

 Encourages students to explore while teacher acts as a tour guide

 Educator as a Curator (Siemens, 2007):

 Dual role as an expert with advanced knowledge of a domain and guide who

fosters and encourages learner exploration

 Educator creates learning resources that expose learners to critical ideas, concepts,

and papers within a field

 Acknowledges autonomy of learners yet understands frustration of exploring

unknown territories without a map 81

 Curator is an expert learner and instead of dispensing knowledge, he creates

spaces in which knowledge can be created, explored, and connected

 Educator carefully balances learner's freedom with occasional injection of content

interpretation

(Geissbrecht, 2007)

There was a fourth school of thought with regard to the educator/instructor as facilitator of informational connections, however the relevant research has disappeared from the databases. This is a real-world example of one of the challenges in discerning the real, solid information from the extraneous bits mentioned above.

In addition to the outlines for a solid educator experience in order to transfer learning to the student, there are also defined roles for the learner in this theory, and the educator must communicate these expectations to their student body either directly or indirectly, in order for their roles to be understood and performed to maximum capacity. They are:

 Learner is at the center of the learning experience, rather than the educator and

institution

 Learner determines the content of the learning, decides the nature and levels of

communication, and who participates

 Develops ability to find relevant information and filter out secondary and

extraneous information

 Learner's capacity to know is more critical than what is actually known (Siemens,

2008) 82

 Learner's ability to make decisions from acquired information is integral to the

learning process

 Knowledge is a creation process and not only a knowledge consumption process

 Learner's ability to see or form connections between fields, ideas, and concepts is

a core skill

 Learning is a cyclical process

o Connects to a network to share and find new information

o Modifies beliefs on the basis of new learning

o Connects to a network to share these realizations and find new information

once more

(Geissbrecht, 2007)

Now that the background has been established and the roles of both the instructor and learner have been defined, the focus will turn to the environment, or classroom setup. As the theory focuses on making connections between prior knowledge and virtual information, the students should be exposed to elements that move beyond the classroom and grant interactive life experience; in this it is comparable to Constructivism. This does not suggest that all current curriculum be abandoned, but that overall the structure of a

Connectivist learning environment will “balance the needs and intent of the designer with the end user” (Siemens, 2007). Siemens illustrates that the design of learning spaces

“should allow learners to . . . form connections and explore areas of personal interest

[and] be balanced with curricular need” (Siemens, 2007). “Education is holistic” and thus 83 balancing the learner’s exploration with the needs of the institution is essential for the whole to work together (Siemens, 2007).

By using learning ecologies, which entail generating an overall sense of community within the learner and the ability to share one’s information and discoveries through usage of blogs, social media, and other collaborative tools such as wikis; combined with the communities that rise from these interactions, and the networks that are created, the

Connectivist classroom begins to take shape. For this model to effectively be utilized, the instructor will set up multiple points of instruction that the students can have access to, using their computers, tablets, phones, and watches. In this, the classroom does not even have to be in a physical space, and learning can occur entirely on the learner’s own time schedule and wherever they are most comfortable. An example would be a Massive Open

Online Course (MOOC) that may be offered by a university such as Harvard; in this

MOOC, the participants can access recorded lectures (or sometimes live ones) through

RSS feeds, livestreaming over channels such as Twitch (which is primarily for online gaming, but can be used for this purpose), access the physical course materials such as exams, quizzes, and notes, and even communicate with the online instructor through email, live chat, or threads in a discussion forum such as Moodle, Canvas, Webcampus, etc. The student also may have the ability to communicate with others that are signed up for the course, so the knowledge delivered may be discussed openly, opinions formed/changed, and conclusions reached/modified. MOOC’s have been used effectively for well over a decade as of the writing of this proposal, and while their design is being replaced with more open-ended, paid approach for course content with sites such as 84

DataCamp, Coursera, and edX, it is still effective in providing quality instruction using the connections people make both electronically and personally. It might be said that by monetizing these courses and how they are delivered, the formula is successful enough to warrant the invasion of Capitalism.

Instructional Design

In addition to the Learning Theory (or theories) applied to this research project, having an instructional design or framework would be of a benefit. In researching the various designs available, the ADDIE system was the one chosen for the development of this project. ADDIE was developed by a research team at the Florida State university in 1975 for implementation by the US Army in their instructional practices (Branson et al, 1975).

It was adopted, implemented, and expanded upon throughout the years to be the basis for much of the instructional design for courses and curricula. This is a broad concept that can be applied to many different areas and subjects such as the medical field (Almomen,

R. K., Kaufman, D., Alotaibi, H., Al-Rowais, N. A., Albeik, M., & Albattal, S. M, 2016;

Cheung, 2016; Khalil & Elkhider, 2016), English (Jones & Jones, n.d.), and online courses (Hess & Greer, 2016; Wang, S.-K., Wang, S.-K., & Hsu, H.-Y, 2008). ADDIE is an Instructional Design model that emphasizes the five (5) key steps in development of a new course or curricula. These five steps and/or criteria are the letters that make up the system’s name:

 Analyze

 Develop 85

 Design

 Implement

 Evaluate

Analyze is where a future curricula creator takes a long hard look and deep dive into the possible need for such a course, lesson, workshop, or curriculum, and will attempt to answer questions such as:

1) What is the background of students in this classroom/situation?

2) What are their needs?

3) What do we wish to accomplish?

4) Will this contribute to the knowledge base as a whole?

There are many varied questions that one can apply to this step, these are shown as examples. In essence, the Analyze step is where projects/ideas are made or broken. If the needs assessment determines there is not a significant need for this curriculum, then it must be abandoned or adjusted to fit with one.

Design is where, after analyzing the needs for said curriculum/lesson/course, one sets about designing materials, lessons, examinations, pacing, schedules, and interactives that will be utilized within the scope of the project/program/lesson.

Develop is the nuts-n-bolts, when these conceptual ideas generated during the Design phase are actually brought to fruition and either made physically or digitally available for use by the instructor and student/participant. 86

Implement is the actual carrying out of the designed and developed lesson, workshop, curriculum, or course.

Evaluate is considered to be the “final” step where the instructor can determine the success of the designed concepts through both summative and formative assessments, however this can be done at any level throughout the process. Instead of considering it like a “waterfall”, it can be visualized as a flow-chart, where at various decision points one can “go back” and change things up, try different approaches, generate new materials and interactives, or just evaluate how one’s hunch is playing out.

Figure 14.

The ADDIE Instructional Design Model.

The original chart of categories used to create the ADDIE design. There are a total of 19 subcategories under the 5 main topics. (Branson et al, 1975) 87

The construction of this project followed this process rather closely, with the researcher analyzing the need for such an intervention. Although there were a few mobile applications developed by instructors as well as outside programmers under an instructors’ guidance which were used within various means of instructional practice

(Zydney & Warner, 2016), there were none that were developed specifically for

Bioinformatics instruction within the life science classroom. In addition, there were none that tackled the emerging technology of gene therapy that is CRISPR. Design and development are described throughout this thesis, and the implementation/evaluation will be within further sections of this report.

Summary

Taking into account all of the facets of this project: integrating technology into the classroom; the mobile application conception, design, and science behind its construction; the learning theories of Constructivism and Connectivism, how they may be applied to facilitate learning, combining with the instructional design of ADDIE to help analyze, construct, and implement this project. As stated earlier, this is a wide-range interdisciplinary research proposal that will determine the efficacy of not only using mobile technology in the classroom, but whether student learners will be receptive to the highly advanced scientific topics presented, and spark a love of learning these concepts beyond what is initially presented.

The idea of teaching the subject of Bioinformatics is a new world for many, with a multitude of higher education institutions not having a course offered, let alone a degree 88 program in the field. With the increasing need for data analysis within the sciences, and the technology (as well as need) outpacing the skilled laborers required to review and analyze this data, there is a crisis looming on the horizon in the discipline. As of now, the state of Nevada has few Bioinformatics-related courses on offer, with those being at the graduate or high undergraduate level. Introducing students to the subject, while also allowing them the ability to use existing technology – which they already own – will help generate the demand for more of these courses, and training of these future scientists can commence as soon as possible. The best way of exposing the students to these concepts as soon as possible, which is why this project’s aim is the introductory classes, rather than the higher, graduate-level. Attracting interest begins at the base of the learning time, and if one student engages with the material, chooses a path that not only gives them a strong career, but allows them to participate in solving some of life’s bigger questions at the molecular biological level.

While the usage of mobile technology has been used in the past, and lightly within the introductory collegiate science classroom, some additional thoughts to consider are: Will the students also walk away with an appreciation for using their already owned and existing technologies in a different way? By combining the best of each of the

Constructivist theory with the relatively new Connectivist, it will assist in the students’ grasp and retention of the data, allowing them a means to access it without an educator or instructor’s guidance.

Methods

Type of Study

The study used within this project’s scope incorporated a quantitative approach as it’s general thrust; the idea being that participants will have an intervention within their classroom environment which can be tested with official scores and numbers to be reported. These numbers were in the form of scores on a pre- and post- test administered prior to and after the execution of instruction and workshops in the four main subject areas incorporated into the mobile application. Students were tested on their prior knowledge, the intervention was applied, and students were re-tested with the same questions as before to indicate level of learning and retention.

In addition, students were asked to complete an attitude survey upon completion of the intervention and testing, which used a Likert-scale to gather general feelings toward the intervention, as well as open-ended short-answer questions for them to indicate improvements, changes, and any other information they wish to share.

Researcher’s Background

The researcher proposing the project defined within holds a degree in Cell and Molecular

Biology from the University of Nevada, Las Vegas (grad 2008), and pursued a Master’s in Education from the University of Nevada, Las Vegas from 2009-2012. Concurrently, they were a classroom instructor for the Clark County School District in Las Vegas,

Nevada from 2005-2013. During this time, they were both a substitute teacher and fully licensed instructor in the Biological Sciences. They achieved Highly Qualified status 90 within their field, scoring in the top 5% of the PRAXIS II Subject-Specific Examination to determine fitness for instruction in the life sciences. Outside of formal instructional environments and licensed positions, the researcher worked in various positions around the city of Las Vegas which had a heavy focus on Education; having held positions for a number of years as Educator/Tour Guide/Docent for Siegfried and Roy’s Secret Garden and Dolphin Habitat at the Mirage Hotel and Casino, wherein they were responsible for educating the general public about the majestic animals and flora that the habitat contained (this ranged from the aforementioned cetacean creatures to the gargantuan white tigers, white lions, various other large cats, and an Asian elephant); additionally, they were responsible for designing and implementing numerous educational programs and projects for the re-opening of the Discovery Children’s Museum in Las Vegas; they worked as a facilitator/educator at Las Vegas Community Centers in the areas of science, reading, and writing; they have been a Science Curriculum Writer for Lincoln Learning

Solutions since February 2015, creating science curricula, lessons, and examinations for a modular online package that not only addresses year-specific needs, but also aligns with the Next Generation Science Standards (NGSS), No Child Left Behind (NCLB), and

Race to the Top (RTTP).

In addition to paid positions, the researcher has also been a volunteer at multiple institutions where education was a central theme; at the aforementioned Discovery

Museum, both in Las Vegas and Reno; at the Child Haven shelter for abused and neglected children in Las Vegas, where they assisted with children who were taken from their toxic home situations through education and keeping them focused on their schoolwork even during the harshest times in their life; to voluntarily hosting tutoring 91 sessions in the areas of science and math throughout the years for family, friends, and their acquaintances.

As of this writing, the researcher is pursuing a PhD in STEM Education from the

University of Nevada, Reno, under the advisement of David Crowther, PhD, with assistance from committee members in the early planning and setup of the applications as well as instruction in the statistical sciences necessary to understand the underlying principles of the applications and Bioinformatics. This project is the epicenter of their pursuit of the degree, and they have called upon their previous experiences and research to put together this comprehensive analytical proposal to test students’ learning ability and retention capacity through integration of technology. The researcher may be classified by many as a “nontraditional” student, as they are a multidisciplinary student, versed in various disciplines, and they utilize their personality and penchant for theater to emphasize their instruction and presentations. They have been asked to teach fellow college instructors, scientists, and other colleagues in the field of Bioinformatics for a number of years, and this is largely based on their effervescent personality and ability to take highly complicated topics and distill them to their essence, so anyone from an expert in the field to someone who had never heard the term prior are able to grasp the concepts.

The researcher is more than qualified to plan, set up, execute, and analyze the project proposed here, and has carried it out to the best of their ability.

Participants

The participants in this study were not selected through any traditional means, as they were students in the Introductory to Molecular Biology Laboratory courses, as well as 92 ancillary courses such as Microbiology at a local Community College where the researcher holds a position as an adjunct professor of Biology. As such, this is considered a Sample of Convenience, and confounding variables such as age, gender, socioeconomic background, educational history, conflicting interests such as the participants’ jobs, family, and other commitments cannot be fully accounted for and removed from the overall equation. A Sample of Convenience is just that: whoever enrolls in the class is a possible participant. They are male or female, ages 18-70, from the lowest economic bracket to an upper-middle-class income, have never taken Biology in their lives or already have their degree and are attempting a secondary baccalaureate. While random sampling with certain restrictions would be ideal, this allows a far more representative sampling of just who would be attending an introductory course in any subject, much less a Biology session.

Sample size

As the groups will be divided into two categories: The Control group, who used more traditional, standard procedures with desktop and laptop computers and browser-based interfaces; and the Experimental group, who utilized the applications developed for analysis of the same data sets, a proper sample size must be calculated to account for ideal measurement.

G*Power is a statistical program created by Franz Faul, Edgar Erdfelder, Axel Buchner and Albert Georg-Lang collaborating from various institutes within Germany. This program takes various input from the user, such as which type of statistical test you would like to perform (t-test, z-test, correlation, linear regression, etc), what you would 93 like your beta power-level to be set, your alpha P-value threshold, and the desired effect size, and return an ideal sample size that will achieve the desired results (Faul, et al,

2007; Faul, et al 2009).

Running the desired results for a two sample, two-tailed independent z-test with an effect size of 0.6 (default) and an α = 0.05, G*Power returns with a result of 76 participants in each group, for a total of 152 total participant data points.

Figure 15.

G*Power Display of Required Sample Size.

So, for this study, the researcher collected 171 participant pre- and post- test results to evaluate the intervention with the application itself. This would provide a more robust result, with higher confidence. For the more qualitative research question about student 94 attitudes and desire for learning with mobile applications, a lower number of participants were collected.

Data Collection

The designing process started simple with the design of a hub area that included the navigation buttons to the various programs and operations (Apps). The construction of each separate App module was a parallel development, as these two have no common efforts until implementation. The next step was the design of the interface itself. This involved less programming and more actual usage of the Android Studio’s functions, as it has a highly interactive and mostly user-friendly graphical user interface (GUI) that makes it rather easy to add, edit, and implement pictures, gifs, designs, action buttons, backgrounds, and many other features.

Finally, the Application was integrated with Google Drive and Google Docs, so the user will be able to access their data on the move and will not have to carry around a file or access their email to then download files to re-upload into the App. Doing this allows ease of use for the user as well as keep security concerns to a minimum, as direct connectivity has been shown to have more encryption. As many people utilize the cloud- based drive for many of their needs, this makes the App much more attractive.

Figure 16.

SCIpac Central Hub and Interface with Google Drive.

Every user will be greeted by this screen upon activation of the application. From here, they can choose which sub-app to undertake: Principal Component Analysis, Test for Normality, Simple CRISPR Analysis, and Probability of Sequence Similarity. The figure on the right displays the choices given when user selects the “Attach” feature within the app; users can tie the app to their Google Drive or internal storage (or both) to access their datasets. Design of Individual Application Suites and Alpha Testing

Normality

Input to this App is a tab-delimited text, comma-delimited text, or Microsoft Excel data file read from either a saved file on the phone/tablet or through Google Drive, and read in through standard Java file reading commands; output will be a histogram to allow the user to quickly assess the shape of the data, a QQ plot, and the p-value of the Shapiro- 96

Wilk test. Graphs are displayed using the graphical packages available to Android Studio such as GraphViewer, GraphPanel, and others that have the ability to generate scatter- plots with submitted data.

Figure 17.

Test for Normality Interface and Results Screen(s).

Users have the ability to type in data manually or attach a preexisting file of data in .xlsx, .csv or.tsv format. They have the option to choose their P-Value threshold for the Shapiro-Wilk test, and to apply a Log Transformation, should they wish. The images in center and right show the possibilities in their results; Shapiro-Wilk results, a histogram, and a Q-Q plot displaying possible normality. Principal Component Analysis

One roadblock is that with the constant updates to the Java platform within internet browsers, the tools mentioned above are having a much more difficult time being allowed on desktop and laptop computers. By extending and changing their security 97 settings/needs, these platforms are pushing the older programs out of usage, and with the lack of updates to the programs themselves, it is increasingly difficult to get them to function properly. The first step was to create a Java package through usage of existing

Open Source code, through transforming a series of commands from another programming language such as R or Python, or directly from the ground up, that will perform all the PCA functions, as that will be the backbone of the program. As with desktop/laptop Independent Development Environments (IDE’s), the Android SDK requires the usage of packages if one wants to use the language for special projects.

Packages are collections of commands or objects that are uploaded into the IDE and then create the ability to be called into usage through other typed commands or objects while coding. Android OS accomplishes a similar function by placing the package within its own folder and then accessing it when the commands are input through user interaction.

Usage of this package, with the dataset provided by the researcher, generates a PCA cluster graph similar to what is seen in Figure 16. The figure is then what can be used in data analysis.

Figure 18. Principal Component Analysis Results Screen

Results displaying the clustering of a set of data from a preexisting experiment. Users also have the ability to run a rudimentary test for Normality in addition to the PCA. Percentage of Variance is also displayed for ease of analysis. As the data input screen is similar to Normality, it is not shown here. 99

Probability Behind Sequence Similarity

The backbone behind the Probability Behind Sequence Similarity App is the binomial formula, and one or two standard PAM and BLOSUM matrices, as well as one nucleotide sequence scoring matrix (more scoring matrices can be added at later times), and the ability to perform a simple BLAST sequence similarity comparison through their database. The user will input a sequence either via an input box or via a text file. The program will read in the sequence, compute the probability of its occurrence first using the binomial formula, and then using residue occurrence rates, such as those found in

(Pevsner, 2009). For the sequence similarity computation, either the sequences are typed in or a file can be read in. Sequence similarity using the simple technique is performed by a simple for-loop summation. The sequence similarity using scoring matrices are computed via a derivative of the dynamic programming alignment algorithm, see Pevsner for an example. Figure 17 has a graphical representation of what to expect within the program.

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Figure 19.

Probability of Sequence Similarity and BLAST Results Screens.

Users have the option to upload a sequence of DNA/RNA/Protein residues or attach a preexisting file in FASTA format. They then can choose to do a probability of the sequence occurring naturally or a BLAST comparison. Results for the probability are displayed on the right, with the BLAST results on the left. The application accesses the BLAST database as if it were a desktop environment, returning a page of results similar to what is seen there, with the same data. Simple CRISPR Analysis

To use the CRISPR tool, the user enters a sequence of DNA of length between L1 and

L2. The second user input will be a non-stop/start codon to be knocked out, replaced, or inserted. The user also supplies a sequence position at which the codon should be deleted.

If it is to be replaced, a third input box will automatically appear to accept the input 101 replacement from the user. The program makes the adjustments and generate a text file to be submitted to a specific user-defined (fourth input box) BLAST query database for comparison with existing mutations and edited genes. The query result with the lowest E- value will be returned.

Upon completion, the text file containing the resulting top query and original input sequence will be delivered along with a graphical representation of the results, with a series of questions detailing what it means. For example, adding in a mutation that is retuned as the above-mentioned preexisting cataract mutation/repair mechanism, the

Application will then display a series of questions such as

 Question 1: “Was there a mutation in the adjusted sequence?”

 Question 2: “Does this mutation exist in any known database?”

 Question 3: “What evidence is provided for either answer?”

 Question 4: “How could you modify the repair to achieve a novel result?”

 Question 5: “If this is a helpful mutation, how can you modify to achieve a

debilitating result?”

These questions are initially the same for each run, with the intention to get the user to think about what changes could or should be made to the original parameters (input file, codon mutation, database search) to get a different result. As these are designed with the intention to spark inquiry, they will not be answered within the App; the user will be required to think on their own, return to the home screen, and attempt multiple searches with modifications. In addition, when this technique is employed within a classroom 102 environment, the instructor created their own specific questions pertinent to the sequences used in the lesson or unit. The ability to alter the given questions will be introduced in future updates. Figure 18 displays the user interface and results of a simple

CRISPR search.

Alpha testing commenced, wherein datasets from generated and published experiments were applied to all functions within the modules. DNA sequences were gathered from databases such as BLAST for the Simple CRISPR Analysis and The Probability Behind

Sequence Similarity Apps. The PCA and Testing for Normality Apps used simple experimental datasets studied in local science labs, as well as standard published datasets in which both PCA and distributions have been established. Alpha testing addressed and/or erased any bugs that crop up from within the android or coding angles.

Beta testing was the implementation of all four Apps within the classroom environment.

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Figure 20.

CRISPR Input Screens and Results

Users can input a sequence of up to 25 nucleotides, choose an up to three (3) nucleotide mutation to introduce, and determine if this mutation is an insertion, deletion or substitution. They can choose where the mutation occurs, and send off for results. BLAST returns shown here. (sequence GCCTGCATGGATTCCATGTTCATG gathered from research on mouse superoxide dismutase in regards to Duchenne Muscular Dystrophy (DMD)). Beta Testing

With development completed, the App running smoothly, the interface up to par, and the integration with Google Docs/Drive effectively working, the next step was to test the App on other researchers’ existing data. The first group approached was current research faculty at the University of Nevada Reno campus who are conducting experiments and research that utilized the Principal Component Analysis and Normality procedures. Sets of data were obtained from these researchers for testing within the App, as they were 104 already published sets and would not affect any of their results or proprietary data collections.

The IdeA Network for Biomedical Research Excellence (INBRE) is a network of states that offer support to underfunded institutions which have large numbers of underrepresented students; students that fall into the category of first-year, lower socioeconomic background, racial/gender representation, tribal colleges, community colleges, and those institutions that tend to be overlooked. This network offered a fantastic opportunity for testing the Apps because the developer was a part of the INBRE network for a large portion of this project’s delivery, which granted more open access to these institutions.

Concurrently, in a local community college setting, the other half of beta testing occurred. This involved the creation of a small, 3 to 5-week curriculum within the undergraduate classroom. Optimum scheduling for this series of workshops was toward the end of the semester, so that the students have as much immediate knowledge pertinent to the ideas presented as possible. During this time the ideas that the Apps address are presented to the students, after which they were led through demonstrations of each particular module and finished up with a more inquiry-based approach where they were encouraged to gather their own data to analyze. An advantage of this particular project is that the developer is an instructor at a local community college, and this mini-curriculum was implemented directly, swiftly, upon completion of development and alpha testing in their own classes. Below is an example of how the Apps could be incorporated into an undergraduate biology or biochemistry curriculum. This was the ideal setting, however it 105 could (and was) altered to conform to the time, setting, institutional/curricula requirements, and/or student aptitude.

Workshop Curricula

Weeks 1-2: Introduction of the statistics concepts

As many of these undergraduates may not have been exposed to the ideas presented within this proposal, there may be a lecture period of no more than four (4) class periods for lecture class, and two (2) periods for a lab section, wherein the concepts of basic statistics, normality, data distributions, and principal component analysis are presented.

This will have to be given at the most introductory level possible, with a gradual increase in difficulty throughout the lectures.

Week 3: Usage of the Statistical Applications

Upon completion of the statistical lectures, the students will then be guided through the usage of the application, with data supplied by the developer/instructor. These data will then be manipulated by students within the PCA and Testing for Normality Apps, specifically. This may take some getting used to and can be adjusted for time. Ideally, this step should take two (2) class periods in lecture or one (1) in lab.

Week 4: Introduction of CRISPR.

As the CRISPR Analysis Application incorporates more molecular biology, this should be a separate unit, taking no more than two (2) weeks total. Week one will be detailing just what CRISPR is and how it functions according to the concepts learned by the class 106 to this point (i.e. DNA, restriction enzymes, types of ‘cuts’, DNA repair mechanisms) and how this technique is used both in the cell and in the world.

Week 5: CRISPR/Probability Behind Sequence Similarity App usage

With the basic understanding of CRISPR, the students can then perform a basic analysis given by the instructor. Upon completion, they will be instructed to recall the statistics previously learned, the CRISPR methods just learned and use the Probability Behind

Sequence Similarity App to put these two ideas together. Explanations that CRISPR is a probability-based technique will also assist with understanding. This should take no longer than two (2) class periods in lecture and one (1) in lab.

Wrap up: The week following usage of the Simple CRISPR Analysis and Probability

Behind Sequence Similarity Apps will be the time wherein the students will give subjective feedback through submission of a survey, as well as objective feedback through completion of a small exam on these concepts. For optimum results, the same test will be given prior to the start of the unit as well as upon its completion. Viewing the before-and-after scores, transforming the data to normal if necessary, and applying a statistical analysis will detail just how effective the overall experience was to the students. The subjective opinions will then be considered and applied, based on the numerical feedback generated. The goal is to make this a crowdsourced experience.

Using mobile devices as educational tools in universities, as well as public and private secondary schools will provide the means to allow access to bioinformatics applications, tools, and techniques not available otherwise. Almost every student in the current 107 environment has a mobile device, whether phone, tablet, or watch. Many schools

(especially those in rural environments)(Correa & Pavez, 2016; Nafziger & Zavadsky,

2015) do not have access to the most advanced or even current or, unfortunately, functional, technology. Mobile devices can offer access to state-of-the art bioinformatics and biostatistics applications via simple access to the internet. Thus, leveraging their ability may be economical and conducive for educational enrichment.

An implementation of several general mobile bioinformatics and biostatistics applications allows educators to teach a variety of bioinformatics techniques (e.g., PCA, CRISPR, sequence analysis) with a much more engaged and active student base (Ekanayake &

Wishart, 2014). By having the students use their own devices, it not only makes them feel more in control of their learning, but also has the element of comfort (Sung, Chang, &

Liu, 2016b). Often students, especially continuing students who are outside of the more expected age range of 18-25 years old, may not feel at ease interacting with a computer or other technology with which they may not be familiar, whether it be something as simple as how to navigate a different operating system or having a faster, smoother response than to what they are accustomed. Using a device of which they have intimate knowledge, as well as dexterity in operation will ease the learning process and help in retention of information.

This utilized the mini-curriculum developed earlier as an assessment to determine success of the integration of mobile Applications into the life science classroom. As stated earlier, the curriculum can be compressed into workshops or divided between separate courses and subjects, to accommodate schedules as well as to reach more students. For example, 108 the Stats portions can be compressed into one lesson, the Molecular Biological (CRISPR and Sequence Similarity/BLAST) into another, or – for an extreme set of circumstances – the entire 5 weeks can be compressed into one long workshop detailing only the barest- bones approach to these topics. All structures were implemented in some form or another throughout this project, meeting the needs and requirements of the institutional curriculum while also getting the central ideas and passions across for the participants/students.

Implementation Phase I

The first phase of Outreach implementation was development of a comprehensive short course covering the bioinformatics and biostatistics techniques involved in the mobile applications developed as presented in chapter II. A three-four class period (or class days, depending on the institution) introductory curriculum in the techniques was created which 1) Introduces the biology, biochemistry, mathematics, statistics, and bioinformatics concepts involved in the methods; 2) Presents usage of each main technique on small datasets in the classroom, and how results should be interpreted by the user; 3) Verifies that all students understand the methods and tools presented.

At the end of the class period prior to start of the unit, a formal knowledge assessment will be administered prior to the actual workshop, this gave the instructor and evaluator an idea of where the students’ aptitudes are situated before beginning. This curriculum was completed before any usage of the application. This formal knowledge assessment consisted of fourteen (14) prepared questions that assessed the students’ knowledge in the four (4) main topics being presented and evaluated within the curriculum and workshops. 109

Many of these questions fall under the category of “Type 1” or “Level 1” questions, they are simple recall questions; however, there are a few which fall under the “Type 2” or

“Level 2” and the students will have to access higher order thinking to be able to answer them. Simply listening and regurgitating the material may be important for first-year introductory students, but it is the intention of this project to not only pass on beginner knowledge, but also spark an interest that may carry over from these workshops. Ergo, it was hoped these second order questions may ignite the ability to think a little higher and cause the participants to wish to answer them with more detail than usual.

Implementation Phase 2

Phase 2 created a workshop to utilize the mobile applications (Apps). The curriculum or workshops consisted of two main parts: during the first, the students will be given a set of data by the instructor and a brief set of instructions and demonstration to use the apps with the data to obtain certain results and interpretations. Upon successful completion, students began the second part of the workshop: they were instructed to locate datasets from a selection of online resources, and then apply the same instructions as before to obtain results and interpret them using the Apps. This required them to download, alter the file type, upload to Google Drive, and then re-download using the apps. Not only did this help them develop skills with the app and technique itself, but also in researching their own datasets, and gathering a greater understanding in the area which they are interested.

Workshops began with the introductory material discussed in Phase 1, the workshops were then detailed to guide the participants to the learning goals. Each of the individual 110 workshops were given, using data retrieved from online databases or voluntarily provided by Primary Investigators. When these datasets were presented to the participants, they were then guided by the instructor to obtain the results necessary for the first step of each workshop.

For the Normality and Principal Component Analysis applications, the data was provided by primary investigators (PIs) from University of Nevada – Reno; these data sets were used within actual scientific research and were from RNA-Sequencing experiments.

There were three (3) main sets of data with which to run initially: one with a small (75) count of data, one with one hundred-seventy (170) data points, and one with around four thousand (~4,000) data points. This provided the necessary tools for the participants to get an idea of how the apps work.

For the Sequence Similarity/BLAST application, the participants received getting two (2) sequences of protein and DNA, each. One of these was a synthetic DNA/protein, one was a sequence taken directly from a living sample at one point. The data was uploaded into the application, and then the participant chose between the idea of the Probability occurring in nature, or the ability to BLAST their sequences, which sends out a detailed search to the National Institute of Health’s BLAST database and returned a result as similar to the desktop experience as possible; there was very little difference between what one will see during a BLAST search from a Windows machine and one utilizing the application. The app also displayed the most commonly occurring nucleotide/amino acid within each sequence, as well as the probability of each sequence occurring with prior knowledge – utilizing the Dayhoff table of known amino acid frequencies – and without 111 such prior knowledge. Without the prior knowledge included the use of the binomial theorem, and each can be used as a comparison for the probability lesson within the workshop or through the participants’ own coursework. In this, it was used as an enforcement tool not only for biological science, but also statistical and mathematic science.

Basic CRISPR Analysis was the app that gained the most attention and enthusiasm, and as such was used last within any setting. This application required the input of a sequence of DNA up to a total of twenty-five (25) characters (letters). These twenty characters/nucleotides were representative of a gene of interest (GOI) that the students will then use their knowledge of CRISPR and mutations in general to change. Most

CRISPR RNA sequences are limited to twenty (20) nucleotides with three to five (3-5) nucleotides used for the Protospacer Adjacent Matrix (PAM) sequence. This PAM sequence identifies the RNA as a CRISPR form and also is massively responsible for recruiting the Cas-9 enzymatic protein. The first sequence was provided to the participants, and comes from an actual published study regarding the editing and repair of a superoxide dismutase mutation that causes muscular dystrophy within mice. The sequence was input by the participants, who then added/deleted/substituted up to three (3) nucleotides in order to cause a novel mutation. Once the data was input, the participant ran the analysis, which was then be compared against BLAST (as with Sequence

Similarity) and the CRISPRFinder database, located in Paris, France. There were two databases accessed, so the participant had a more robust response, and was able to determine if their particular mutation is a novel one. Both databases presented a return, whether there is a sequence found or no. Novelty is the goal here, and if they found that 112 their mutations did return previously known sequences, they were required to go back into their CRISPR home screen, input a new sequence, and repeat the process over again.

As stated in Chapter II, there was questions the participants will be asked to help them come to the conclusion of whether to re-mutate or not.

After practice with each given set of data – whether genomic sequences or RNA Seq

Data, the participants were then required to seek out their own data sets (time permitting) and then run their own inquiry-based analyses on these new batches of numbers and letters. Scaffolding the instruction such as this alleviated any issues that arose with regards to user interactions, questions, and confusion.

Control Group

Up to this point, the sections of implementation focused on the applications developed for intervention, and the Control Group classes followed the exact protocol, with the exceptions that the tools used for implementation were the classroom desktop computers or the students’ own laptop systems.

 For Normality, the students visited the website for Statistics Kingdom, found at

http://www.statskingdom.com/320ShapiroWilk.html. This website has a very similar

interface to what is found in the application, and returned similar results in the same

form (S-W test, P-Value rejection or acceptance, histogram, and Q-Q plot).

 For PCA, students visited the ClustVis website, found at https://biit.cs.ut.ee/clustvis/,

which is the only available PCA tool on the web without paying for a subscription or

learning how to program in R, Java, or another language; this facilitated the students’

ability to perform a PCA and recieved similar results to the application. 113

 For Probability and BLAST, students visited the NCBI BLAST website and entered

in the data files provided, found at: https://blast.ncbi.nlm.nih.gov/Blast.cgi. In

addition, they also performed rudimentary probability tests using the binomial

formula provided and hand-calculating them through Windows 10’s included

calculator (there is not a sequence probability calculator widely available).

 For CRISPR analysis, the students hand- “mutated” the sequence provided and

entered it into the NCBI’s BLAST site, as well as CRISPRFinder, and noted if they

have made a novel mutation. They then followed previous procedures and continue

modifying their sequence and testing it against the databases. The results were exact

to what was found in the application.

Surveys

Mentioned earlier, in addition to the quantitative pre- and post-tests administered to the participants to gauge the knowledge gained during the workshop intervention, participants were requested to complete mixed-methods survey on the website surveymonkey.com. This survey consisted of Likert-scale questions where participants delineated how much they themselves knew about the subject, what they thought of the intervention, and open-ended questions at the end requesting them to detail any changes, improvements, what worked and didn’t. The purpose wass to elucidate a general feeling for how the integration of mobile technology is either a helpful or hindering addition to their college experience. 114

The surveys were used to determine where to take the intervention in the future, as well as assisting in making any modifications deemed necessary for the success of the intervention and the topic of Bioinformatics in general within a college setting.

Technology Used

Participants interacted with the datasets provided using technologies that are already present in their lives, whether those be the more traditional desktop/laptop settings or the mobile application on mobile devices. As such, the specs are detailed here:

 Control Group: Used the Dell 2015 model desktop computers available within the

introductory laboratory classroom. However, there are only six (6) of these

systems, so participants were encouraged to bring their personal laptop computers

to participate in the workshops. Since these were their personal systems, it was

difficult to get an accurate measure and description of every particular laptop. The

websites used for data visualization and manipulation are designed to run on the

lowest-end of machines, so this did not affect their ability to work with the data at

all.

 Experimental Group: The application was installed on twelve (12) RCA Voyager

7” tablets, each holding 1.2GHz processors, 1GB DDR of system memory, 16GB

of onboard storage memory, touchscreen capability with 1024 x 600 resolution,

and built-in 802.11 WiFi/ Bluetooth. These tablets also run the Android 8.1 Oreo

Operating System, on which the application itself was designed. They all were

linked up to a “throwaway” email account for Google Drive access, and the

participants were able to download the datasets directly from that account. 115

As research shows that the Android/Apple usage split in America is 47%/53%,

respectively, and the maximum number of students allowed in the laboratory

course is twenty-four (24), this matches the research and additionally allowed

students to work in groups of at least 2, following the Constructivist and

Connectivist theories.

Coronavirus Interruption

During the 2020 Spring Semester, the world was wracked by the novel coronavirus

(SARS –COV-2) outbreak and normal school functions were shut down almost completely, forcing faculty, staff, and students to work remotely as much as possible. As such, the researcher had to create a means to continue collecting data, and came up with the following plan:

The intervention was done remotely, as well as the dataset manipulations by students.

Since this is designed with a Constructivist bolstered by a Connectivist approach, this is ideal for a remote session or two to allow the participants to engender themselves to the concepts. Workshops were recorded and uploaded to the participants’ class learning management system hub (in this case, Canvas), participants took the pre-test and submit it to the instructor, view the interventions, follow the instructions on data manipulations, and submitted the post-test at the end. Each of the interventions’ recordings were created to be similar to what would be experienced within the classroom environment, and took approximately the same amount of time to peruse and complete. In addition, the data was collected anonymously, as participants were given the exact same form provided to in- class participants, and were instructed to not apply their identifying information in any 116 way (name, class #, etc). this also applied to naming of the file itself (participants were discouraged from naming their file “WilcoxPostTest”, for example). These files were assigned the pre- and post- test numbering that would accompany an in-person situation, the files were then downloaded from the learning management system, printed out as hard copies, and the files deleted. The assignment portals were also deleted within the

LMS to avoid identifying metadata. Participant anonymity was kept, as well as removing any biases the researchers may have if they knew who submitted which file.

Data Analysis

Assessment of the Implementation

The students were subjected to a summative assessment before and after the course for comparison purposes. This short assessment was given in the form of an examination consisting of short answer and short essay/labeling questions which are as comprehensive as possible regarding the PCA, probability, CRISPR analysis, and testing for normality concepts presented within the Apps. Multiple Choice and True/False questions were considered for this implementation; however, it was determined that the evaluations would be better served by the students demonstrating true retention through physically writing out their knowledge with short answer, mini-essay type questions. While there is one which is a simple “Circle the Correct Answer”, it is not a true multiple choice, as the students still had to evaluate which is the correct answer through observation and critical thinking utilizing previous – or in this case recently acquired – knowledge in order to determine whether or not the Q-Q plot is indeed correct. 117

Additionally, the students submitted a survey at the end of the course to assess the students’ comfortability, ease-of-use, and knowledge generation through use of the application. This survey was designed as an ordinal-answer, Likert-scale, with answer choices ranging from “Strongly Disagree” to “Strongly Agree”, with “Disagree”,

“Neutral”, and “Agree” rounding out the choices. There was also a section wherein the students can provide opinions in their own words about what they liked, what they disliked, and notes for what could be improved.

Assessment Control Group

As a baseline (control group), Phases 1-3 and the Assessments were implemented within a second classroom: this class was taught using existing online tools that perform similar operations as those in the mobile Apps developed here. This class – or series of classes - was considered the “control” and was given the same assessment before and after the course just as in the App-based course.

The before and after assessments were compared in both the experimental and the control groups first to measure extent of learning in each. A statistical t-test of means was performed on the non-essay (objective) questions across each before and after pair to easily measure with statistical significance whether students performed better after taking the course. Additionally, the assessments between the experimental and the control groups was examined similarly to determine, with statistical significance, whether there was a difference in learning rates between the course using the Android Apps and the course presenting the web-based tools. See above section on Sampling with G*Power analysis to reiterate what type of test was done on the data sets. Of course, the desired 118 outcome was that teaching via the Apps yields a greater impact (measured by a better student understanding) when teaching topics include analytical tools discussed here.

Both groups (Control and Experimental) were assessed by running an unpaired t-test on the differences between the pre- and post- tests. Each set of data had the post-test scores subtracted from the initial, with the difference then represented by a value. These values were then run through the t-test algorithm to determine if there is a significant difference within the means of two groups. If significance is seen (α < 0.05), then the null hypothesis shall be rejected, with the alternative supported.

The surveys were collated and analyzed using a Frequency Distribution table, to account for how often participants answered a certain question a certain way, and to then display attitudes towards integration of these technologies into the college science classroom to teach introductory Bioinformatics.

Identifiers

Student participation was as anonymous as possible, with the pre-and post-tests not having any identifiers at all; students were given a number, rather than a name or other identifiable characteristic. These tests were prepared by the researcher prior to the participants receiving them, with each participant receiving a numbered exam, and participants were instructed to remember their particular number for the post-test.

In addition, the actual analysis of the exams was performed at a more removed time from the administration of said exams. This allowed the researcher time to collectively

“forget” who was handed which exam during the time of administration. In effect it 119 reduced any bias the researcher may have formed with the participants, and allow for a freely objective data analysis.

To allow for the coronavirus shutdown during the 2020 Spring Semester, the data collection and analysis was done remotely with the same amount of anonymity, as seen above in Data Collections.

Limitations to the Methods

Sampling

Sampling for this project was done through those who have enrolled in the Introductory

Biology Laboratory course at the local Community College. As such, there was not an opportunity to randomize and/or select for the optimal isolation of confounding variables that may interfere with learning and retention of the material. This is otherwise known as a Sample of Convenience, in that the participants are the ones who choose to be in it rather inadvertently.

Participation

As this is a subject opened up within an introductory Biology course in a community college setting, there may need to be an impetus for participation, particularly with the

Likert-Scale survey taken after the intervention and examinations are complete. Many participants may want to have a reward for their time, effort, and willingness to give up those valuable commodities. While offering rewards runs counter to not only a

Constructivist/Connectivist classroom setup, it also brought into the fold ethical questions of whether or not their answers may be skewed with regards to the idea they will get 120 extra points, a gift card, or other type of trade for their participation. So, the researcher did not offer such.

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Results

Overview of Research Project

The purpose of this project was to determine the effectiveness of a series of

Bioinformatics workshops presented to groups of introductory biology students at the community college level. It was designed around the idea that delivering these ideas, concepts, and topics through usage of students’ mobile devices would not only assist with the learning of said topics, but also with enjoyment and desire to continue such study.

Research questions and hypotheses were created along these lines, as well; with them focusing on whether the implementation of mobile technology would assist with participant learning and retention of the material presented, as well as evaluating how those involved within the study viewed their participation and if they found anything of value within the idea of using their existing and currently owned mobile technology.

The methods used within this study were to present a pre-test of fourteen (14) questions, to assess participant prior knowledge of the topics to be presented. The workshops are then given to the students, with an introduction to the overall idea of Bioinformatics, the statistical (Normality and PCA), and finally the molecular biological (Sequence

Similarity and CRISPR). This could have taken the form of in-person classroom-based workshops (pre-COVID-19), or online pre-recorded videos presenting the same materials

(Post-COVID-19). Participants were then given the post-test, which was the exact same fourteen (14) questions as the pre-test, this in an attempt to determine how much of the materials the participant had learned throughout the workshops. A survey was then offered at the end of the entire series, with their opportunity to answer 9 Likert-scale 122 questions about the material, their preferences to the technology used, and if they may wish to try the type of tech offered to the other group.

Participants were placed into two groups: The Control group, which utilized existing technologies such as online websites to assist with learning and retention of the materials, and the experimental – or App group, which utilized the mobile technology and software created for this project. These participant groups were selected prior to each semester began, and were chosen at random; the researcher would select based on how many participants were needed in each group. For example, if there were an even number of courses being taught by the researcher that semester, 2 would be assigned to Control, 2 to

App. If there were an odd number of courses taught, the group which needed more data points were given preference prior to the start of the semester; i.e. 3 classes, however

Control group had many who chose not to participate in previous semesters or who had multiple people drop the course prior to implementation, then 2 out of those 3 classes were chosen to represent the Control. Each group was selected at random and were not changed once the semester began.

Recruitment of participants, process detailed earlier, occurred during the semesters of

Spring 2019, Summer 2019, Fall 2019, Spring 2020, Summer 2020, and Fall 2020 – with this final group used to gather more qualitative survey data. This made the total time close to 18 months. Spring and Summer 2019 provided the study’s first participants, with the bulk of participants coming from the Fall 2019 and Spring 2020 class sessions, with the researcher having a total of 8 classes between that time period, due to initial acceptance of instructional duties as well as taking over courses from faculty who left 123 their position shortly after the semesters began. Summer 2020 gave the final few participants needed to provide the proper numbers for statistical analysis.

All willing participants were given the pre-test, the ensuing workshop(s), the post-test, and the request to complete the after-class survey. In all, 171 pre- and post- tests were recovered. 55 total surveys were collected over the course of the project’s duration. Data was collected and placed into an Excel as well as an SPSS analytical environment.

Data Analysis

The analysis of the data was a two-pronged approach, as there was quantitative data collected in the form of the tests given before and after the workshops, and the more qualitative survey data coming after the workshops’ completion. The written/typed responses for the tests were put through statistical analysis, the surveys are evaluated using frequency tables and

Statistical Analyses

For the quantitative aspect of this project, the independent variable was the workshops and instruction’s effectiveness given during said workshops. The dependent variable was the change in participant scores (if any). After collecting the data from participants, the variables were input into statistical analytical software for analysis. Pre-test scores were subtracted from post-test scores, with the resulting differences tabulated and those differences were then run through an Independent Student’s t-Test. These date were unpaired, from two separate groups that did not interact with each other, and were unaware of the other’s differing tools, unless notified by the researcher at the end of the 124 workshops. Data had already been collected by that point, and as such would not affect the quantitative results of the pre- and post-tests.

t-Test Analysis

With 82 participants in the Control Group - the group of participants which used the currently existing websites and tools for these concepts – and 89 participants in the

Experimental Group, or the participants who used the applications, the total came to 171.

This is consistent with the G-Power analysis ran at the project’s beginning, delineating there should be a certain number of participants within each group; this amount met that goal. Breaking down these groups’ pre- and post-test data into the score differences between said pre- and post-, the data were then organized and put through the Student’s t-

Test to test for the difference between means. Two-tailed was chosen as a means of providing more to the result, as it splits the alpha, in this case 0.05, into half encompassing the upper and lower bounds of the data set.

Early analysis through Microsoft Excel’s t-test algorithm gave a return of p < 0.001, and further analysis of the data through SPSS gave the same result. The mean for the Control group was 7.52 (SD = 3.38), while the mean for the App group was 9.43 (SD = 2.39).

Both had a standard error of their means of .371 and .254, respectively.

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Table 2.

Group Statistics of the Independent t-Test.

Workshop Effectiveness N Mean Std. Error Control 83 7.52 (3.38) .371 App 89 9.43 (2.39) .254 Note: SD in parentheses to the right of means.

The results of the t-test proper between the two groups gave us the results p <0.001,

regardless of whether or not the variances were equal or not. Degrees of freedom were

170 with equal variances, 146 without. Levene’s F score was 13.945, with a significance

of p <0.001. Calculating for the Cohen’s d value, the effect size was determined to be

0.651.

Table 3.

Results of Independent t-test Run on Differences Between Pre- and Post-Test Scores

Levene's Test t-test for Equality of Means

Mean Std. F Sig. t df Sig. Diff Error Differences Equal 13.945 .000* -4.295 170 .000* -1.909 .444 in pre- and variances post-test assumed scores Equal -4.245 146.606 .000* -1.909 .450 variances

not assumed Note: * = p<0.05. 2-tailed significance test.

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Figure 21.

Comparison Between the Means of Differences in Pre- and Post-Test Results.

Pre-test results were subtracted from Post-test results and the differences run through a Student’s t-test. The means of those differences are displayed above, with the Control group representing existing technology employed, and App group representing the mobile technology used. Normality was assumed, but running through the SPSS software, it was determined that the Control group’s data was indeed normal, with the Shapiro-Wilks p-value of 0.055 higher than the threshold of 0.05, rejecting the alternative and failing to reject the null hypothesis that the data comes from a normal distribution.

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Figure 22.

Normality Between Participant Groups.

Representative distributions of data collected between Control group (left) and Experimental App group (right). The App group was skewed to the left, with more scores falling within the higher point value range. Running the entire data set through a normality check gave a Shapiro-Wilks result of p <0.001, with an overall left skewness to the graphical representation. It was considered to run the data through the Application developed for this project itself, as it has the capability to do so, and return accurate results – as determined by Alpha testing – however it was decided that may introduce some bias into the results.

As there were non-equal variances, shown by the large F-value of the Levene’s Test and it was determined to run a Mann-Whitney U test of Medians to shore up the results. The

Mann-Whitney U is a non-parametric test used when assumptions such as Normality and homoscedascity (equal variances) are not present or violated. As Normality was not present within the groups, and the significance of Levene’s test was p<0.001, and the group n’s were unequal, the rank-sum test was chosen.

The results of the Mann-Whitney U test were similar to that of the independent t-test run at the start of analysis, in that the significance was p<0.001, the Z score showing an 128 absolute value of 4.108, indicating overall significance of the data. Data can be found in figure 22. Calculating the Mann-Whitney U’s effect size, gives us 0.64, only 0.01 lower than the Cohen’s d registered for the t-Test, and showing a high-effect on the data by the workshop.

Table 4.

Mann-Whitney-U Resultsa

Differences in test scores’ result Mann-Whitney U 2361.500 Wilcoxon W 5847.500 Z -4.108 Asymp. Sig. (2-tailed) .000* Note: a.= Grouping Variable: Workshop Effectiveness, * = p<0.05

Surveys

Survey data was collected using the 10-unit questionnaire given over the website surveymonkey.com. The initial 9 questions are Likert-scale ranging from Strongly Agree to Strongly Disagree, with Neither Agree or Disagree in the middle. Some questions were geared toward the participants’ prior knowledge, and as such had different indicators, but were along the same scale; for example, rather than Strongly Agree, the response was

Extremely Familiar. All of the responses were on the 5-point scale, outside of the final question, which was designed for participants to input their opinions on what they liked, disliked, and what could be used to improve the instruction or the tools used.

The surveys were answered at a lower rate than the pre- and post-test, with (as of this draft) 43 total surveys returned, 31 of those belonging to the Control group, and 12 to the 129

App group. The responses were coded into the categories of 5 = Strong, 4 = moderate, 3

= neutral, 2 = opposite moderate, 1 = opposite Strong. This was to maintain consistency throughout the analysis.

Table 5.

Frequency of Responses by Question of Likert-Scale Survey for Control Group.

Question 5 4 3 2 1 1.Bioinformatics is a totally new topic for me 17 10 2 2 0 (54%) (32%) (7%) (7%) (0%) 2. My familiarity with statistics before this 0 7 12 5 7 program can be described as: (0%) (23%) (39%) (16%) (23%) 3. My familiarity with Sequence 0 2 11 9 9 probability/comparisons prior to the program (0%) (7%) (36%) (29%) (29%) can be described as 4. My knowledge of CRISPR prior to this 1 1 3 13 13 program was (3%) (3%) (10%) (42%) (42%) 5. I am more familiar with Data Screening 2 22 5 1 0 Statistics concepts after using this program (7%) (71%) (16%) (3%) (0%) 6. I am more familiar with sequence similarities, 3 24 2 2 0 their process, and how CRISPR functions after (10%) (76%) (7%) (7%) (0%) this program 7. The use of existing desktop/laptop 6 20 4 1 0 technology in this program helped my (19%) (65%) (13%) (3%) (0%) understanding 8. I would be interested in seeing more types of 6 16 5 4 0 programs, lessons, and workshops that use (19%) (52%) (16%) (13%) (0%) desktop/laptop computing technology in my science classes 9. I would like it if there were a way to integrate 7 14 9 1 0 mobile technology into learning this and other (23%) (45%) (23%) (3%) (0%) science topics

Table 6 illustrates the amount of responses per question within the Likert-scale for the group of participants who used the pre-existing technologies online and/or with desktop/laptop computers.

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Table 6.

Frequency of Responses by Question of Likert-scale Survey for the App Group.

Question 5 4 3 2 1 1. Bioinformatics is a totally new topic for me 12 9 3 0 0 (50%) (38%) (12%) (0%) (0%) 2. My familiarity with statistics before this 0 3 5 9 7 program can be described as: (0%) (12%) (21%) (38%) (29%) 3. My familiarity with Sequence 0 0 2 7 15 probability/comparisons prior to the program (0%) (0%) (8%) (29%) (63%) can be described as 4. My knowledge of CRISPR prior to this 0 0 2 9 13 program was (0%) (0%) (8%) (38%) (54%) 5. I am more familiar with Data Screening 2 15 4 3 0 Statistics concepts after using this program (8%) (63%) (17%) (12%) (0%) 6. I am more familiar with sequence 8 10 4 2 0 similarities, their process, and how CRISPR (33%) (42%) (17%) (8%) (0%) functions after this program 7. The use of the mobile technology and 11 9 3 1 0 application in this program helped my (46%) (38%) (12%) (4%) (0%) understanding 8. I would be interested in seeing more types of 15 6 2 1 0 programs, lessons, and workshops that use (63%) (25%) (8%) (4%) (0%) mobile computing technology and applications (apps) in my science classes 9. I would feel more comfortable using 3 6 11 4 0 desktop/laptop technology in learning this and (12%) (25%) (46%) (17%) (0%) other science topics

Table 5 above displays the amount of responses recorded for the Application group’s questionnaire. The amount of these are lower, as the participants chose not to respond early on in the early groups. The survey was made available to both groups of participants and everyone were invited to complete and submit, however the numbers were still low.

The surveys were designed in an effort to gauge participant attitudes toward the knowledge presented, the prior knowledge coming in to the workshops, their desire to 131 learn more based on what they experienced, and their attitude towards using the type of technology their group had. The final question was offered as a means to gauge their interest in learning the opposite way, although they were unaware the other version existed (unless notified by the instructor upon completion of the entire process).

The final question of the survey was where we aimed to learn more about their attitudes, in their own words. This area was a short-answer question that asked, “Please comment here on anything you, as a participant liked, disliked, enjoyed, wished to see more of, or wanted to remove.” This gave participants a more active role in their replies.

A sample of replies for the Control group are:

“This section was extremely informative and opened my eyes through many different avenues of SCIENCE!” “I really enjoyed the Normal distribution and the Q-Q”

“I really enjoyed the statistics portion and Shapiro-Wilks test but became quickly confused going over BLAST, and pretty lost going over CRISPR. I would be very interested in learning more about using statistics in relation to biology.”

“Thank you for introducing me to this vast world of discovery through the "marriage of life science and technology", as you put it. To say that I am intrigued would be an understatement...but all of that information was also a little overwhelming. And not necessarily in the "this is giving me anxiety" sense, but in the "there is an abundance of knowledge out there that I have yet to explore" sense. Parts that fascinated me include: the DNA sequencing and similarities between differing organisms; the amazing technology that there is out there to decipher, decode, and compare DNA (I'm still just completely amazed at how much detail and action there is going on on a microscopic and subatomic level...and I know I've only been introduced to just a GLIMPSE of what is out there); and I'm mostly intrigued by the implications that this technology and these studies could have to cure, treat, and prevent diseases and disorders. Parts that I "disliked"... That statistical math equation. HAHA! Goodness...maybe 132

after I take my math class, those things will be a little less intimidating. I would love to put different specimens through a sequencer to observe and analyze the data in order to see where certain species are extremely similar to each other (although, at present, I wouldn't know what to do with that information once I got it)”

“I wanted to learn more about the BLAST thing.”

“I liked the pre and pst test concept. I feel as though it helped me actually retain more of the knowledge learned from the videos.”

“I'm fascinated with gene sequencing and CRISPr, so this was very informative, and will be good to have a few new resources to go back to in the future.”

“Liked!”

All were not positive, with some not comfortable with the influx of information, some liking one aspect but not the other, and others not answering the question altogether.

Some not-so-positive examples are:

“I liked learning about the topics, yet felt it was way too much for me at least to understand and not be overwhelmed in trying to learn it in a few days or even understand it.”

Likewise, the mobile surveys also had the return of some answers, but not all. A representative sample:

“This was really fun! I've never been able to use my phone to learn something in a class, except for reading notes and other stuff.”

“I really liked using my phone for something in class!

“I enjoyed everything about it” 133

“I found the CRISPR portion of this to be very interesting, and I'm curious if other biology courses (either at TMCC or UNR) go more in depth about it at all” “I liked the phone app! I want more of this kind of thing”

“WOW! This was so fun! It was totally over my head, but I loved the ideas! More phone work, plz!” “I liked the ideas of Crisper and using my phone was a great help. It was really nice to be able to use it for something besides Instagram.” “This was all a bit overwhelming, especialyl so early in the semester. But I liked the concepts and the professor had a clear love for the stuff, so I'd take it over again with him. Love for the topics are what helps people like me learn these things, because if the person is not interested, why should I care? Using the app was fun, and it would be nice to have more of this in class. It could save me some money, too.” Not all found it pleasing:

“I was not going to answer this, but I found it to be a waste of time really. This was not necessary for the class.” “I'm not sure what this was all about really.”

Although one participant did not find it to their liking:

“Even with help from the video, I still had trouble understanding how to work the program and in the end I was in tears struggling to work it because I just didn't understand how it was meant to work.”

The video mentioned was the recorded workshops utilized to get around the coronavirus quarantine during the Spring, Summer, and Fall semesters of 2020. As detailed previously, the workshops were all recorded and uploaded to the Learning Management

System (LMS), branded Canvas, for viewing and the participants were also given all materials necessary to complete the workshops, the tests, and the survey.

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Ancillary Analyses

There were not any official ancillary analyses performed for this project.

Participant Flow

As iterated earlier, participants were not chosen by traditional research means, and were students who were enrolled within the courses where the workshops were presented.

Groups were selected for implementation randomly prior to the semester beginning; for example, if the researcher had 3 classes, the number of students enrolled was evaluated, and the class was assigned to be “Control” or “Variable” (later changed to Experimental, finally altered to App), and the participants were informed of the research they would be partaking, with the option of not participating. Participation – nor non- would not have a negative impact on participants’ grades or perception from the instructor in any way, and this was stated prior to any administration.

In addition, the researcher “guest instructed” courses at the college, in other classrooms under other faculty. These visiting workshops recruited participants with whom the research was unfamiliar, and who may have had more prior knowledge. These participants were placed into the Control group prior to the implementation, and consisted of Microbiology courses, as well as one Anatomy and Physiology course.

These courses are 200-level, introductory tier, but for students who already have a background in the sciences. 135

The workshops were not given until the end of each respective semester, so as to allow the participants’ knowledge from the course allow them to have at the very least a rudimentary understanding, with the material presented not completely off their radar.

In total, 171 participants took the pre- and post-test, with 82 in the Control group, and 89 in the App group. Many did not participate, or participated with one test, but were absent for the other. Some showed interest, but then declined once the workshops’ time came.

Those who did not actively contribute to the data pool were still encouraged to attend and participate in their own way.

Baseline Data

Baseline data was not gathered for this project.

Statistics and Data Analysis

Intent-to-treat was not used within the scope of this project. However, as explained above with Participant Flow, the participants were asked to participate, with zero negative consequences for denying, and those who chose to be part of the collection then had the workshop applied. Participants who did not take part were still invited to do so, however their data and responses were not recorded for both data richness and ethical practices.

Adverse Events

While there were not any adverse events caused by the workshops, tests, or surveys on the participants themselves, the coronavirus pandemic of 2020 could have interfered in the data collection, as it hit during the middle of the Spring 2020 semester, and continued throughout the writing of this dissertation. The researcher devised a way to still allow 136 participation and collection of data, while keeping anonymity and integrity of the data.

This also had the purpose of causing a distance between the researcher and the participants, as there was not an ability to form a rapport with the participants, reducing the possible bias. 137

Discussion/Conclusion

Mobile Technology in the Introductory Biology Classroom

With the results displayed previously, it can be determined that the research questions and their resultant hypotheses were answered definitively, at least within the scope of this particular project. Revisiting the questions, we see that:

RQ1: Is there a significant difference in the means of learning progress scores in statistics/bioinformatics concepts between Community College General Biology and

Microbiology students who used desktop / laptop computers and those who used the mobile suite?

And

With their resulting hypotheses:

Hypothesis 1: Utilizing the mobile application will have a significant impact on the learning of Bioinformatics materials within the Introductory Community College Biology

Classroom 138

Hypothesis 2: There will be a difference between the attitudes of those who wish to use computers vs mobile devices, with the preference going to mobile.

Mobile Applications Vs Traditional Computing

Looking at the data presented, it can be said that the research questions and hypotheses were supported. The comparison of the Control Vs Application groups’ submissions of pre- and post-tests were scored and the differences between the two were used as the basis for examination. The differences were the post-test scores subtracted from the pre- test scores, with any answer that was close to a correct answer given. Participants were directed to write/type “No Answer” rather than leave any question blank if they did not know the response to a question, both within the pre- and post-test realm. This allowed the researcher the ability to gather that the participant actually participated, even if they had zero knowledge of any of the topics presented within the pre-test, specifically. For example, if a participant felt overwhelmed and left everything blank, how would the researcher know if the person had even read the questions? Having them write something, even “No Answer”, showed that they at least performed the minimal requirements to move forward with the workshops.

Each test was evaluated for the initial amount of No Answers vs actual answers given, with the answers being considered ‘correct’ if it was remotely close to the information provided. For example, if a participant responded with “I have no idea, but I had fun in your class!” (an actual response), that was considered a non-answer, while something as simple as “CRISPR stands for Clustered Regularly Spaced Repeats”, while not completely correct (the full answer would be “Clustered Regularly Interspaced Short 139

Palindromic Repeats”), shows that the participant at least partially paid attention, used the technology, and learned something. As this level of material is a bit advanced for many entering the course, let alone these workshops, it was determined that getting “close” was good enough to constitute an answer.

There was a good amount of answering of the more “easy” questions, those that were familiar, or could be more familiar based on the context clues i.e. Question 2: “What is

Genomics?”, a student completely unfamiliar with that subject could generate a competent guess as to the definition of the study of genes or genomes. However, there were questions designed specifically for content knowledge gained form the workshops themselves, such as being given a small, 4-letter protein sequence and asked to determine the probability of that sequence occurring using the binomial theorem. That particular question was “No Answer”-ed by almost all participants, on pre- and post-test results, and was a more precise measurement of how much actual material was absorbed by these participants.

All of the questions were created to be as broad and accessible as possible, while still retaining the design of the study and allowing participants to be challenged.

Every question was reviewed, scored, and tabulated, for the pre- and post-tests. “No

Answers” were counted as blank, and blank questions were also considered no answer; this came down to participants either not listening/reading the directions, not caring, or possibly refusing to follow any instruction from the researcher/instructor. As stated before, any answer closely related to the correct or desired response – as these were all short-answer type questions, with the exception of circling a proper Q-Q plot – 140 participant responses were given a wide range of possibilities for ‘correct’ answers.

Participants were writing/typing their responses in their own words, after all, and as long as they appeared to grasp the concept, it was accepted. Again, pre-test scores were subtracted from post-test scores, with the differences used as the determining factor.

The differences were then collated into data sets, one for the Control, and one for the

Application group. Comparing the two sets, it showed a significance of p<0.001, and the threshold established at the start of the project was p<0.05, so this supported the hypothesis of the Application’s usage showing a difference within the workshops and the topics’ implementation in the classroom. The mean of the Application group was 9.43 out of 14, and the Control group’s mean was 7.52 out of 14 questions (Table 2). This represented the increase of answered questions from the pre- to the post-test, so on average the Control group increased 7.52 answered questions from the pre-test, while the

Application group increased 9.43 answered questions (Table 2). There were significant degrees of freedom, allowing for the fact that there were such a large number of participants. Taking into account the statistical significance, as well as the Mann-Whitney

U test (Figure 22), the Cohen’s d effect size being .65, which rounded up to .7 has it fall within the medium-high range, indicates a strong correlation between the usage of the application vs usage of traditional laptop/desktop methods. The Effect Size is an indicator of just how strong the relationship between the differences of the groups are.

Normality was not achieved within the App group, but the Control group seemed to get close (Figure 19), with the resultant Shapiro-Wilk test not showing significance for the

Control group (p>0.05), rejecting the null hypothesis, and supporting the alternative that 141 the data comes from a normally distributed set. The App group, however, had a large left skew, as the collected differences in scoring favored the higher end of the range (0-14).

There were few outliers in this data, with only 2 data points falling out of the 4-13 rand noted within the collected differences (Figure 20).

Additionally, as Normality was assumed, but not shown to exist within the entirety of the dataset, the researcher looked at the Levene’s F-value and resultant significance (Table

2), showing 13.945 and p<0.001, respectively, and deduced according to the rules of said test that homoscedascity, or equal variances, could not be assumed. While equal variances are not completely required to run and accept a test of independent sampling, combining that with the normality violation it was chosen to run a non-parametric test to determine significance, and to keep the validity of the data intact. The test chosen was the

Mann-Whitney U test, also known as the Wilcoxon rank-sum test, which does not require the assumptions of normality, equal variances, or anything else restricting the parametric test.

Running this test through the appropriate software, it was shown (Table 3) that the probability of any value of the Control group has the ability to be equal to that of the

Application group to be false, displayed by the significance value falling below the 0.05 threshold (p<0.001). By having such a low p-value, the null hypothesis – that the medians of the data sets are equal – is rejected and the alternative is supported. The alternative being that there is indeed a significant difference, or change, between the two groups of data, independent of the point chosen. So, the means as well as the medians are considered to be significantly different. 142

This inevitably supports the hypothesis and research question that using mobile technology does improve student learning within an Introductory Community College

Biology Classroom setting.

Attitudes of Mobile Applications in the Classroom

The second research question and hypothesis was interested in determining the attitudes of the students/participants toward using mobile technology within the Introductory

Biology classroom at the Community College level. The survey described within the methods section was given at the end of the entire series of workshops, and allowed self- assessment of participants’ prior knowledge, their knowledge gained, if they enjoyed or liked using the technology (whether standard computers or mobile), and what they personally enjoyed, liked, did not like, or wished to change within the project’s scope.

This survey was a series of ten (10) questions, and would only take a few minutes at most to respond.

However, there was not the response that was hoped for by the researcher in this project.

While there were 171 total participants within the study of differences between pre- and post-testing, only a total of 55 responded to the surveys; 31 for the Control group, and 24 for the App group. It is unknown why so many chose not to participate in the final survey, and the researcher shall not deign to speculate why during this discussion and further proceedings. While it would have been desirable to get more results, the ones provided still offered quite a valuable glimpse within the psyche of Introductory students’ attitudes toward mobile technology, the subject matter, and their willingness to use mobile or currently existing technology. 143

Many suggested, regardless of their group, that they were unfamiliar with many (or all) of the topics being presented; 54% in the control, 50% in the App groups strongly agreed that Bioinformatics was a completely new topic for them; 23%, 7%, and 3% in the control group, 12%, 0%, 0% in the App group expressed moderate knowledge of the topics beforehand; this represented questions # 2, 3, and 4 – Statistics, Sequence

Similarity, and CRISPR, respectively. The majority of respondents selected Slightly

Familiar (16%, 29%, and 42% for Control; 38%, 29%, 38% for App) or Not Familiar at all (23%, 29%, 42% for Control; 29%, 63%, 54% for App) to those same three questions.

(Tables 5 & 6)

Almost all of those found their knowledge base increased after attending and participating within the workshops, again regardless of group. For the questions dealing with increase in knowledge, questions 5 and 6, the Strongly Agree (10%, 19% for

Control; 8%, 33% for App) and Moderately Agree (71%, 76% for Control; 63%, 42% for

App) all showed significant improvement and confidence with the material. (Tables 5 &

Table 7 below displays the three questions within the survey that dealt specifically with the tools employed in the learning process. Almost all appeared to enjoy the methods utilized, and those within the control group did not express an overtly strong positive desire to utilize mobile technology (only 23% strongly agree, 45% Agree, and 9% neither agree or disagree, Table 7), while those within the App group showed a stronger willingness to continue with the mobile technology (63% Strongly Agree, 25% Agree, 144

Table 7), but not a clear desire to utilize traditional desktop/laptop methods (46% neither agreed nor disagreed, Table 7).

Table 7

Group-Specific Questions from the After-Workshop Survey

Control Group Question 5 4 3 2 1 7. The use of existing desktop/laptop 6 20 4 1 0 technology in this program helped my (19%) (65%) (13%) (3%) (0%) understanding 8. I would be interested in seeing more types of 6 16 5 4 0 programs, lessons, and workshops that use (19%) (52%) (16%) (13%) (0%) desktop/laptop computing technology in my science classes 9. I would like it if there were a way to integrate 7 14 9 1 0 mobile technology into learning this and other (23%) (45%) (23%) (3%) (0%) science topics

App Group Question 5 4 3 2 1 7. The use of the mobile technology and 11 9 3 1 0 application in this program helped my (46%) (38%) (12%) (4%) (0%) understanding 8. I would be interested in seeing more types of 15 6 2 1 0 programs, lessons, and workshops that use (63%) (25%) (8%) (4%) (0%) mobile computing technology and applications (apps) in my science classes 9. I would feel more comfortable using 3 6 11 4 0 desktop/laptop technology in learning this and (12%) (25%) (46%) (17%) (0%) other science topics

Taken together with the quotes from Question 10 listed previously, shows a clear preference by the App group to continue using their mobile technologies within the classroom setting. There was an overwhelmingly positive response to using their mobile technology for something other than the activities they were used to. 145

In addition, there was an intense excitement and joy for the material itself; many respondents stated they were interested in learning more about Bioinformatics and were going to seek out other methods, datasets, materials, and courses to learn more. Later in this section, there will be other examples of students having their interest sparked in the topics presented.

So, it can be said that the subject matter, the presentations, workshops, tools, and methods utilized within this project had a positive effect overall, with the student participants leaning toward a more mobile environment, wherein they can use their already existing and owned technology to examine the data around them, and/or create levels of inquiry on their own.

Discussion on the Methods and Implementation

Regarding the methods an implementation employed throughout this research study, there were a few iterations and adjustments that the study had to undergo. As with any program, it was piloted at first to gather brief information and determine the flow of materials delivered. This was done through the implementation of the workshop(s) as outside presentations for faculty throughout the state with the traditional computing methods; the App had not been designed, or was in the process of being designed and developed, during this time. The researcher had used their connections with the INBRE network, described above, to assist with this implementation. This helped the researcher smooth out any rough patches and even figure out means of streamlining the presentation of information for participants who may not be familiar with the subject matter at all. 146

These participants did not fill out the pre- and post-tests, and were just attending as a means of learning new, or improving on current, techniques.

Post-Pilot Program

After that piloting, the program was initiated properly as an official Pilot Program during

Spring 2019 session. That session was where the first set of Pilot data were collected, and used as a basis for moving forward. This was the first group of participants to take the pre- and post-tests, and initial results showed great promise, with both groups scoring within a normally distributed dataset, however the Mann-Whitney U results did not show significance. That was expected, as the totals were low at that time, and the data collection hadn’t started properly. This Pilot data was included within the complete dataset due to the methods not changing at all; for example, they all received the same tests, with the same 14 questions, and the same survey to fill out afterward. It should be said, however, that the courses comprising this Pilot group were not the most participatory group of students, particularly the Control sample for that time. People within that class-group were of the mind that they were there to get a grade and that was it; participation within anything extraneous was a chore to have occur. Of course, they are free to have that mind-set and opinion, it is only noted here that when dealing with human participants, there can be outside variables that cannot be accounted for, no matter how much one prepares.

On that note, the following courses were all rather smooth and went without incident for the most part. Participants were informed of their participation, what it meant, what it was for, and their ability to deny or back out of participating at any time in accordance 147 with the submitted methods to the University of Nevada, Reno Institutional Review

Board (IRB). There were not that many who chose to not perform the workshops and tests/survey, however there were a few that started but did not complete, or wished to participate after the pre-test had already been given. These students had their data collected, but it was not included in the overall analysis. Researchers felt it was important that they feel included, even without a final analysis of their work.

Many of the data points were collected within the Fall 2019 – Summer 2020 series of semesters, with the bulk of those coming between the Fall and Spring. The researcher offered the workshops to other faculty in an attempt to not only gather more data points, but also engender said faculty to the idea of different teaching methods and new subjects to introduce to their students. There were a few who accepted, however only one did not utilize the researcher as the instructor; it was a condition of using their classes that they be the one giving the instruction. Approached faculty on the whole did not feel prepared to give lessons on such topics, even with coaching by the researcher. This was a new wrinkle that the researcher had considered, but was not expecting to encounter within the field itself, as in their experience post-secondary instructors are rather excitable when new methods are offered. So, there was a massive uptake of Control group data from those outside courses during the Fall 2019 semester, with the App groups falling under the purview of the researcher themselves. If faculty were not comfortable teaching topics, having them fiddle and attempt to understand the intricacies of application download, installation, and implementation were considered off the table. 148

This meant that, in order to become a “guest speaker” for outside class environments, the material had to be reevaluated and shrunk from five 1.25-hour lecture sessions (or 2 3- hour lab classes) to something that would not be so invasive on the faculty allowing the research to occur. The material was condensed into 2 1.5-hour lab sessions and presented during the lab sections of those particular courses; there were 2 Microbiology courses and one Anatomy and Physiology class.

Coronavirus Interruption and Adaptations

With the onset of the Spring 2020 semester, the researcher had a great idea of where the research was going and had a smooth delivery system in place. When the SARS-CoV-2 virus hit the shores of the United States in January/February 2020, and went unchecked for so long, it caused the closure of all schools – including where the researcher was conducting research and instruction. When the schools physically closed, all instruction was moved online, so as to enhance and encourage social distancing, allowing the medical staff working the frontlines to not be overwhelmed with patients and “flatten the curve” of infections.

What this did is cause the research project to adapt to the new environment, and that was detailed within the Methods section above. Videos of the workshops were recorded, delving into the topics as if they were being presented within the classroom. Nothing was changed from the condensed versions of the workshops described previously, except for the delivery being asynchronous, rather than real-time. Participants were given the pre- test before access to the videos was permitted, to allow for answer integrity, although the researcher could not prevent any from using their search engine skills and if they wished 149 to appear “smarter”, they could have looked up any answer and provided them electronically. The workshop videos were then opened for viewing after the deadline for submission of the pre-test; participants were instructed to not place any identifying information on their submissions, as the instructor would know who submitted by their online portal.

Videos were then available for a certain period of time (10 days), where the participants were instructed to view and follow along as best as possible. These videos included the introduction to Bioinformatics, the Normality/PCA topics, and the Sequence

Similarity/BLAST/CRISPR topics, respectively; three videos of approximately 30-60 minutes detailing every facet of the ideas necessary to walk away with rudimentary knowledge of the project. In addition, there were demonstration videos of approx. 45 minutes for each group where the internet-accessible websites for data analysis and the application download/install/setup/analysis procedures were walked through for all participants. Data files used during the in-person classes were also uploaded to the learning management system Canvas, in addition to the application. apk file for download onto their Android-compatible technologies.

Upon the completion of the 10-day period, participants were then given the post-test to fill out and submit again through the online portal. They were then offered the after- workshop survey to finalize their participation within the program.

Data was then collected and collated online, through the online submission portals. If there was identifying information, the researcher removed it prior to analysis, in order to keep anonymity and remove potential bias. For example, if they had known the 150 participant, they may have been more inclined to be more forgiving to the answers provided; this removed that ability as much as possible. Of course, if a participant answered with their “voice”, i.e. typed as they may have spoken or as colloquially as they did within their communications with the instructor/researcher, then identification – at least within the researcher’s own experiences – may have been slightly possible.

The remainder of the procedure is outlined within the methods section, so will not be re- iterated here, however that process was followed, and the resultant data collated, analyzed, and compounded within the overall datasets.

Relevance of Study to Research

Looking back on the theoretical foundation of the study, we were interested in looking at the implications of using the Constructivist learning theory (Vygotsky, 1978) combined with the Connectivist learning philosophy (Siemens, 2005, 2006, 2007, 2008; Downes,

2010) to determine the effectiveness of the instruction delivered to these introductory

Biology students at the Community College level. Additionally, the topics presented were not a usual assortment of concepts given to students at the 100 or 200 level within that sphere.

Normality may be taught within a 300-400 level statistics course, or a 200 level

Biostatistics class, but rarely outside of that particular sphere. Principal Component

Analysis is not taught outside of specialized graduate-level courses, unless an instructor feels particularly adventurous. Sequence Similarity is reserved for some introductory courses, and is at the instructor’s discretion; although probability frequencies of these sequences occurring within a pre-determined algorithm or without prior knowledge was 151 not found to be used within any classroom setting, more than likely being reserved for the specialized arenas of the science laboratory on a need-to-know basis. Finally, CRISPR is still a fairly new concept, only being understood for the last 6-7 years at the most. In fact, the research scientists Jennifer Doudna and Emmanuelle Charpentier won the Nobel

Prize for their discovery of and contribution to the knowledge pool of CRISPR in

October, 2020. This has shed new light on the subject, above the attention focused on the technique after the “CRISPR babies” – whose embryos were manipulated to alter the

CCR5 gene in an attempt to generate an immunity to the Human Immunodeficiency

Virus (HIV) – did so in 2018. Having the ability to present this information not only as an abstract concept but as a demonstrable, concrete example (or series of examples) will assist with understanding and reduction of fear of these new ideas. These are all concepts and topics to which many biology students may never be exposed, and this type of intervention/workshop was designed to provide just that.

In-Person Classroom Setup

While within the in-person classroom, the instructor/researcher set up the classroom and workshops to follow along with the Constructivist classroom (Vygotsky, 1978), it was setup to deliver instruction to develop a base of knowledge, these topics mentioned which students may have never seen prior. It was then scaffolded to allow participants to explore on their own, in accordance with the classroom described in the Literature

Review (Tam, 2000). Participants also worked in groups of heterogeneous students, rather than alone. Their learning was self-directed, as they voluntarily chose to participate, and had the opportunity to leave at any time without penalty. Targeting the 152

Zone of Proximal Development was considered, so as to not overwhelm the participants too much; although this may not have been completely successful, these are intense subjects, after all. This knowledge was also personal to the students, as they were encouraged to pursue their own questions during the workshops (with any spare time they may have had) as well as after the workshops and even class semester was over. The instructor/researcher gave the rough outline, students followed, and then were directed to come up with their own research elements in their own time. In doing, so, the researcher hit almost all seven (7) of the questions/tenets posed by Honebein (1996). This was evidenced by at least two students contacting the researcher post-semester to inform them that they did indeed look further into a genetic question they had, using the tools introduced within class. One student even shared that they were considering entering the field of Health Informatics after participating in the workshops. The instructor then did what they could to connect that student participant with the correct people at the local

University to get the process started. At least mention, the student was indeed pursuing an

Informatics bent to their already established goal of becoming a medical practitioner. In essence, the Constructivist approach was successfully implemented within the scope of this project.

There were some expressions of fear amongst a few of the students within the classroom, however. The researcher conducted an informal question and answer session with four

(4) students after class in late Fall 2019 semester, and asked them a series of questions regarding utilizing mobile technology within the classroom. They stated that they may not be disciplined enough to be able to handle the type of responsibility of using their phone for class without looking at their mail, their social media, or texting friends and 153 family. This is consistent with the research explained at the beginning of the literature review with the fears of integrating technology in any classroom setting, whether that be

TV, Radio, Computers, or Mobile Technology. They also expressed a security fear, or the ability to stay on track and how will the instructor monitor their progress and activity, which are concerns that can be addressed in future setups and implementations of this curriculum.

Online Classroom Setup

As stated multiple times throughout this manuscript, the 2020 SARS-CoV-2 pandemic interrupted the in-person classroom setting, necessitating the move to remote learning and usage of the online environment exclusively. This was almost advantageous to this particular project, however, as it then moved into a more Connectivist-style (Siemens,

2005: Siemens, 2006; Siemens, 2007; Siemens, 2008; Downing, 2010) classroom environment. Described earlier were the MOOC’s (Massive Open Online Courses), where anyone can sign up to take pre-recorded courses from a number of institutions, most of the time completely free of charge. In effect, the courses taught at the

Community College (and university, and public/private primary and secondary school) level were now MOOC’s.

This allowed the researcher to focus more on the Connectivist approach for the classroom setting. While it was not voluntary, this online setting gave the opportunity to research how students/participants can interact with technology on a more active field. Translating the in-person curriculum to the online arena proved to be much smoother than anticipated; video technology was provided by the schools to use, and the Learning 154

Management System was already being employed by the instructor for use within the class. It was only a matter of shifting everything to that environment.

This allowed the student participants to engage with the material in a self-directed learning thrust, allowing a socially and technologically enhanced network and allowed complex learning to occur (Ertmer & Newby, 1993). In this sense, the instructor/researcher acted as a concierge (Bonk, 2007) as well as a curator (Siemens,

2007), both guiding as well as fulfilling a dual role as educator and expert, ready to answer emailed questions should they arise.

As the department required instructors (especially during the altered Spring 2020 semester) to teach asynchronously, this truly setup the Connectivist classroom approach.

By having little to no real-time interaction with the students/participants, it pushed the issue that students are their own learners, their desire – or lack thereof – is what drives their successes, failures, and learning processes. Students and research participants had to become connected with the technology, and in turn with the material; not only for the scope of this project, but also for their normal coursework.

What is interesting is that there were not any comments or concerns about the design of the App; which led the researcher to conclude that either a) they designed something so well that users found it intuitive enough or b) that the participants did not notice.

Whichever it was, researching effective user interface design seemed to have been successful. 155

In terms of creating a Constructivist, Connectivist approach to learning the introductory materials of Bioinformatics, the structure was sound and appeared to be successful, given the results of the overall study.

Bioinformatics

The thrust of this particular project was the introduction of the topics of Normality,

Principal Component Analysis, Sequence Similarity, and CRISPR analysis, under the umbrella of Bioinformatics, into the Introductory Biology Community College classroom. However, the theories behind these topics were discussed in depth throughout this manuscript, and as such do not need to be referred to once again. However, there are ways these theories can be implemented with the technology within a science or mathematics classroom outside of this project. They were:

Normality

As an example outside of the scope of this study, while in a lab course at the collegiate level, a collection of data can be obtained from a series of die rolls, with the students collecting and tabulating results of how many times each number comes up after N number of rolls. The students would write down a tally each result, and total at the end.

Utilizing the computer, either in the lab itself, a laptop brought from home, or the phone/tablet itself, the students will then recreate their tally table within a text file. After completing the file, it will be uploaded to Google Drive or directly to the App itself, and 156 then run through the tests for normality. Once they get their results, they can then see the distribution and answer an additional question sheet provided by the instructor. (pg. 38)

Principal Component Analysis

As this is an important statistical tool that handles data collected from, e.g., groups of organisms, the introduction into a college biology curriculum could be made during a unit on ecology. When the topics of communities, ecosystems, and populations are brought into the course, the students can then be given a short lecture on how scientists collect data; occasionally there is a complementary (and compulsory) lab section to a course that also would be useful for introducing this concept. Once the basic tenets are understood by the students, the instructor could then walk them through a prepared dataset to demonstrate the ability of the App. Upon mastery of the Application, the students can then be encouraged to gather their own datasets outside of the classroom and run them through the same routine; this could also encourage them to form hypotheses, as it would be important to establish just what type of data one should be collecting. (pg. 43)

Probability of Sequence Similarity

As this is an Application designed around incorporating both statistical probability as well as rudimentary sequence analysis, this was implemented within a unit on the history of protein discovery. One could discuss Sanger’s sequencing of insulin and Dayhoff’s sequencing of multiple proteins to determine her matrices. This led into a short introduction to simple probability and how to use it within life science, with a question and answer period between instructor and students. Once the idea is grasped, the 157

Application was introduced, with a demonstration by the instructor, followed by a period where the students will fiddle around with the Application. (pg. 50)

Simple CRISPR Analysis

To bring this high-level science into the undergraduate classroom, the application developed in this research is based on simple principles to emulate the CRISPR techniques. This is a higher concept molecular biology technique than many students may be familiar with, so this was used in the classroom by integrating it within a unit or lesson that is focused on DNA repair; generally, toward the end of the overall macromolecule study unit. In that time, students learned about the basic makeup of the molecules of life such as DNA, RNA, proteins, lipids, and carbohydrates. Ergo, when the particular area of

DNA repair is introduced, CRISPR was shown and this application can give a more hands-on understanding of the concept.

Each of these examples could be expanded upon, shrunk, altered, redesigned, or incorporated as-is depending on the skill level of the instructor as well as the aptitude of the students involved in the classroom. The applications of the Application are limited only by the instructor’s imagination at this point.

Limitations of the Implementation

Limitations and possible error has been mentioned throughout this manuscript, with the special section dedicated to it within the methods chapter. However, there are some others that should be addressed here.

Control Group 158

When looking at the resultant data of the differences between the pre- and post-test scores, there was a moment of doubt, as the scores looked a little too good, and the significance a little too convenient.

After a little time thinking, it was put forth that there may have been some error within the Control Group due to the types of students chosen for those trials. While the selections were done prior to the semester beginning, there were a large amount of participants within those class rosters that had a previous degree in another field; not necessarily science-related, but having both the discipline as well as a font of knowledge the more traditional Intro student may not possess. For example, in the Fall 2019 courses taught by the researcher, there were a total of 9 returning students with baccalaureate degrees who were wishing to pursue medical careers (nursing, physician’s assistant, or otherwise), and quite possibly their pre-tests were ones with higher scores, making their post-test – even if the score was perfect – smaller than the difference between exams a perfectly “green”, or totally new, student would achieve. Another example was a few participants from Spring 2020 who scored perfectly on the pre-test, apparently well- suited for the subject (or searched every answer), and therefore their post-tests were also perfect, making the difference score a zero.

There was also the possibility that the participants from the 200-level guest-teaching courses had higher scores on the pre-test and therefore had very little room to maneuver to have a large difference, however they constituted a small size of the overall sample data, with few choosing to participate or the class size itself being smaller than expected.

159

Instructor

In order for this to be a complete and fair assessment, the instructor/researcher must take a step back and analyze their own bias for the material. As this was their pet project to earn a terminal degree, there may have been a tad more enthusiasm when giving the App- focused workshops over the traditional desktop/laptop. The instructor went back over the videos recorded for the online workshops and did not notice any difference between the demonstrative videos (which were the only different videos, both groups had the same content videos). They also re-evaluated to the best of their memory their performance during the in-person class sessions, and to their recollection, there was not any difference.

However, that is of course a biased viewpoint and should not readily be counted as valid.

Whatever the case, the sole other instructor who used the materials in their class had similar scores to what was found under the researcher’s guidance.

Time

The limitations of the timing of both the workshops’ performance as well as the time given to participants during online classes (roughly 2 weeks between pre to post-test) also could have interfered with the participants’ ability, and with a large portion of the Control group coming from the online classes, that could’ve affected the results. It could’ve also given the App groups more time to research how to do the functions as well as using the technology required (phones and tablets).

Overall, these limitations and possible error may have contributed, but it is the belief that it may have interfered a minute amount, at most. 160

Conclusion

Based on the available, analyzed data, it can be stated that Research Question 1, and its accompanying hypothesis, were supported. All data came out significant, on every test run against it, with all null hypotheses being rejected and the changes in the means as well as medians owing to a statistical significance and not random chance. The Effect

Size was large enough to corroborate this conclusion, as well. There was a significant difference between using the Apps over the more traditional methods

In regards to Research Question 2 and its resultant hypothesis, it can be stated clearly that the participants who responded preferred the App over traditional methods, with the written answers of the surveys leaning more towards mentioning the Application more so than the Control group appreciating the usage of the traditional browser-based approaches. There were a significant number of people within the App Group who expressed strongly they wished to see more mobile-based technology used within the classroom, but the Control appeared to have an equal reaction regarding access to both the traditional as well as a mobile possibility. Although, almost every participant that took the poll came out learning more than before, and many expressed interest in the subject, including those mentioned specifically elsewhere. So, regardless of the technology, the enthusiasm for the subject matter has been awakened in these participants, and it is hoped they will keep pursuing these ideas during their further coursework and study. 161

In addition, taking into account the events of the year 2020, wherein the Coronavirus

Pandemic – and American’s subsequent handling of such – caused the massive shift for many school districts and post-secondary institutions to the remote learning model, creating access for these students to information is swiftly rising to the top priority for many educators. Connecting instructors, students, and their support systems such as parents, significant others, and even possibly their own children (if they are Community

College students of more advanced ages), with information as well as each other can really assist with bringing learning forward into this “new normal”, should the fallout from this large, long-term experiment of remote learning leave a more lasting impact.

Introducing new ideas, especially when those ideas revolve around remote-style learning through usage of computers or mobile technology will help to engage students and possibly give them a feeling of more control over their learning and retention. Having the ability to learn “on the go” will become of more importance the further these new modalities are pressed into service for the “new normal”.

Overall, this project is one small step in showing how allowing students the ability to connect to information, formulate their own experiments, and come to conclusions with a constructive, scaffolded learning approach can be very beneficial. More studies can and should be done to shore up this simple conclusion, in addition to monitoring the need for these types of scientific subjects’ instruction within the Introductory Community College classroom. Computational Biology/Bioinformatics is something that cannot be ignored as we increase our computational abilities in that exponential curve during the next few years/decades. Our technology has already outstepped our knowledge in how to use it; we must catch up, and fast! Bringing these two facets together is paramount – and ideal – for 162 our future as a contender within the scientific sphere. The first major step is getting students excited for learning the topic, and that begins here.

Future Directions

With the conclusion stated, there are a few directions this project can take for future usage and implementation.

First, upgrading/updating the application and integration with the Apple platforms would be a grand idea. This would allow all users access to the Application and be able to participate in the workshops.

Second, adding onto the Application to create more Bioinformatics-related content such as phylogenetic tree-building to demonstrate evolutionary links, designing and creating an RNA-sequencing module to assist with learning that advanced technique, and integrating more statistics, as that was one of the written responses’ highest requests. This hub-area can integrate any Java-based code, so it could be easy to pick up an idea, figure out how to code it, and implement within the hub. This could become something similar to what Wolfram-Alpha does with its learning hub, but this would be Bioinformatics and

Biology-centered.

Third, perhaps re-imagining the workshops into more of a semester-long curriculum, introducing the pieces a bit at a time, and expanding it to include so many other

Bioinformatics topics. This could easily become a course in and of itself, using the application in combination with traditional methods to open students’ eyes to the possibilities in science research. Far too often it’s envisioned as something that must be 163 done in a lab, wearing a white coat, or outdoors living in a tent cataloguing wildlife and plants. The researcher has seen multiple students’ eyes widen when they realize they can research at home, on their couch, running analyses on datasets they can scrub from the massive amounts of online databases available.

Fourth, running this particular study again and again to get more data would definitely be helpful, as this was a small set overall, even if it did meet the parameters set by G*Power and the requirements of statistics testing. Having more qualitative survey data would also be helpful in determining the desire for these subjects (and more) in the area of

Bioinformatics. Knowing what the students think and want in their classes can help educators and even scientists who teach classes create better curricula that can cater to their needs. It can also spark the research interest in those who may never have considered it before.

Fifth, working on the security issue would be of a high importance. There have been recent issues with “hacking” of mobile applications to steal users’ data, and the idea of having an application cause such an issue for one’s group of students would not be something any instructor would wish. This would involve more extensive research and collaboration with professional app developers to figure out how to close loopholes and broken code that allow these types of attacks. For now, the App is only distributed by the researcher on this project, and the participants were instructed to delete the app soon after the implementation if they have security concerns.

Sixth, approaching more active members in the scientific community for their evaluation and approval of the application suite as a viable research tool would be ideal. In this, the 164 researcher can then clearly state that this is a tool not only for education, but for actual scientific analysis as well. It would be great if we could secure a strong scientific desire to use such techniques on their own research.

Finally, setting up a new methodology and examining the differences between the groups who were able to participate within the workshops and examinations while in-person vs those who were unable to do so due to coronavirus and were instead made to participate remotely (online). Would there be a significant difference between those in-person learners vs online? Examining if the App vs Control settings within those parameters could yield a different set of results.

The success of this research project is only the first step in the development of a larger curriculum, course, and creation of accessible technology. With the lack of a proper

Bioinformatics course series, let alone a degree (or even certification program) in the state of Nevada, getting students charged up and excited for learning these materials can help push that in the right direction. Creating more lessons, tools, and curricula focused on learners taking charge of their own development and learning, while connecting to each other and information using technology, can really bring learning into the 21st century and beyond. 165

References

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