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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 by Joseph Wilcox Dr. David Crowther, Advisor December, 2020 © Joseph M. Wilcox Dec 2020 All Rights Reserved 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 i 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. iv Dedicated to Catherine Schmader & Eric S. Gustafson Without their support and inspiration, I would not be here. v 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 vi 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 xi 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.
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