DIGITAL ETHICS in HIGHER EDUCATION 2020 Make Decisions with Confidence
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Different Hype Cycle Viewpoints for an E-Learning System
IOSR Journal of Research & Method in Education (IOSR-JRME) e-ISSN: 2320–7388,p-ISSN: 2320–737X Volume 4, Issue 5 Ver. II (Sep-Oct. 2014), PP 88-95 www.iosrjournals.org Different Hype Cycle Viewpoints for an E-Learning System Logica Banica1 1Faculty of Economics, University of Pitesti, Romania Abstract: This paper uses Gartner Group’s Hype Cycle as an analysis tool for the e-learning platform integration process at the University of Pitesti. The platform was installed three years ago and during this time there were many points of view (by major user groups) regarding the usefulness and adoption rate. Based on this fact, according to us, the e-learning Hype Cycle has two graphical representations, two variation curves that have the first part identical but differ significantly in the following two stages, differences that disappear in the end. Based on our analysis, we found necessary to improve the management strategies, to better motivate the teachers in order for them to reach the same level of interested shown by the students in using the system, and thus bringing the two educational groups to the same opinion. We have depicted all this evolution and the different points of view by the Hype Cycle curve, that should be only one for the 5th year of system presence in our institution, a convergence reached a bit too late in the IT world, where software solutions have a limited life cycle. Keywords: Hype Cycle, e-learning, academic collaboration I. Introduction This paper is a new approach of our research on learning systems related to the academic domain: e- learning. -
DJ Patil [email protected] Former U.S
Contact 650-450-6288 (Work) DJ Patil [email protected] Former U.S. Chief Data Scientist San Francisco Bay Area www.linkedin.com/in/dpatil (LinkedIn) twitter.com/dpatil (Rss) Summary DJ Patil has held a variety of roles in Academia, Industry, and Top Skills Government. Data Science He is Head of Technology for Devoted Health, a Senior Fellow at Machine Learning the Belfer Center at the Harvard Kennedy School, and an Advisor to Public Policy Venrock Partners. Honors-Awards Dr. Patil was appointed by President Obama to be the first U.S. Chief Department of Defense Medal for Distinguished Public Service Data Scientist where his efforts led to the establishment of nearly Young Global Leader 40 Chief Data Officer roles across the Federal government. He The World's 7 Most Powerful Data also established new health care programs including the Precision Scientists Medicine Initiative and the Cancer Moonshot, new criminal justice Gen Flux Cover Story reforms including the Data-Driven Justice and Police Data Initiatives 36 of tech's most powerful disruptors that cover more than 94 million Americans, as well as leading the national data efforts. He also has been active in national security and Publications for his efforts was awarded by Secretary Carter the Department of Use of the Breeding Technique Defense Medal for Distinguished Public Service which the highest to Estimate the Structure of the Analysis “Errors of the Day” honor the department bestows on a civilian. Local Low Dimensionality of Atmospheric Dynamics In industry, he led the product teams at RelateIQ which was acquired Extracting Envelopes of Rossby by Salesforce, was founding board member for Crisis Text Line Wave Packets which works to use new technologies to provide on demand mental Data Driven, Creating a data culture and crisis support, and was a member of the venture firm Greylock Identifying Low Dimensional Nonlinear Behavior In Atmospheric Partners. -
This May Be the Author's Version of a Work That Was Submitted/Accepted for Publication in the Following Source: Dedehayir
This may be the author’s version of a work that was submitted/accepted for publication in the following source: Dedehayir, Ozgur & Steinert, Martin (2016) The hype cycle model: A review and future directions. Technological Forecasting and Social Change, 108, pp. 28-41. This file was downloaded from: https://eprints.qut.edu.au/95588/ c Consult author(s) regarding copyright matters This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the docu- ment is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recog- nise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to [email protected] Notice: Please note that this document may not be the Version of Record (i.e. published version) of the work. Author manuscript versions (as Sub- mitted for peer review or as Accepted for publication after peer review) can be identified by an absence of publisher branding and/or typeset appear- ance. If there is any doubt, please refer to the published source. https://doi.org/10.1016/j.techfore.2016.04.005 The Hype Cycle model: A review and future directions The hype cycle model traces the evolution of technological innovations as they pass through successive stages pronounced by the peak, disappointment, and recovery of expectations. -
Science & Technology Trends 2020-2040
Science & Technology Trends 2020-2040 Exploring the S&T Edge NATO Science & Technology Organization DISCLAIMER The research and analysis underlying this report and its conclusions were conducted by the NATO S&T Organization (STO) drawing upon the support of the Alliance’s defence S&T community, NATO Allied Command Transformation (ACT) and the NATO Communications and Information Agency (NCIA). This report does not represent the official opinion or position of NATO or individual governments, but provides considered advice to NATO and Nations’ leadership on significant S&T issues. D.F. Reding J. Eaton NATO Science & Technology Organization Office of the Chief Scientist NATO Headquarters B-1110 Brussels Belgium http:\www.sto.nato.int Distributed free of charge for informational purposes; hard copies may be obtained on request, subject to availability from the NATO Office of the Chief Scientist. The sale and reproduction of this report for commercial purposes is prohibited. Extracts may be used for bona fide educational and informational purposes subject to attribution to the NATO S&T Organization. Unless otherwise credited all non-original graphics are used under Creative Commons licensing (for original sources see https://commons.wikimedia.org and https://www.pxfuel.com/). All icon-based graphics are derived from Microsoft® Office and are used royalty-free. Copyright © NATO Science & Technology Organization, 2020 First published, March 2020 Foreword As the world Science & Tech- changes, so does nology Trends: our Alliance. 2020-2040 pro- NATO adapts. vides an assess- We continue to ment of the im- work together as pact of S&T ad- a community of vances over the like-minded na- next 20 years tions, seeking to on the Alliance. -
Horizon Scanning Opportunities for New Technologies and Industries
Horizon Scanning Opportunities for New Technologies and Industries by Grant Hamilton, Levi Swann, Vibhor Pandey, Char-lee Moyle, Jeremy Opie and Akira Dawson July 2019 © 2019 AgriFutures Australia All rights reserved. Horizon Scanning – Opportunities for New Technologies and Industries: Final Report Publication No. 19-028 Project No. PRJ-011108 The information contained in this publication is intended for general use to assist public knowledge and discussion and to help improve the development of sustainable regions. You must not rely on any information contained in this publication without taking specialist advice relevant to your particular circumstances. While reasonable care has been taken in preparing this publication to ensure that information is true and correct, the Commonwealth of Australia gives no assurance as to the accuracy of any information in this publication. The Commonwealth of Australia, AgriFutures Australia, the authors or contributors expressly disclaim, to the maximum extent permitted by law, all responsibility and liability to any person, arising directly or indirectly from any act or omission, or for any consequences of any such act or omission, made in reliance on the contents of this publication, whether or not caused by any negligence on the part of the Commonwealth of Australia, AgriFutures Australia, the authors or contributors. The Commonwealth of Australia does not necessarily endorse the views in this publication. This publication is copyright. Apart from any use as permitted under the Copyright Act 1968, all other rights are reserved. However, wide dissemination is encouraged. Requests and inquiries concerning reproduction and rights should be addressed to AgriFutures Australia Communications Team on 02 6923 6900. -
Case Study Questions
Full-Circle Learning MyLab™: Learning Full Circle for Marketing, Management, Business Communication, Intro to Business, and MIS BEFORE CLASS DSM's, pre-lecture homework, eText MyLab Writing AFTER Space, Video CLASS Cases, Quiz- Decision zes/Tests Sims, Videos, DURING and Learning CLASS Catalytics MyMISLab™: Improves Student Engagement Before, During, and After Class Prep and Engagement • Video exercises – engaging videos that bring business concepts to life and explore business topics related to the theory students are learning in class. Quizzes then assess students’ comprehension of the concepts covered in each video. • Learning Catalytics – a “bring your own device” student engagement, assessment, and classroom intelligence system helps instructors analyze students’ critical-thinking skills during lecture. • Dynamic Study Modules (DSMs) – through adaptive learning, students get personalized guidance where and when they need it most, creating greater engagement, improving knowledge retention, and supporting subject-matter mastery. Also available on mobile devices. • Business Today – bring current events alive in your classroom with videos, discussion questions, and author blogs. Be sure to check back often, this section changes daily. • Decision-making simulations – place your students in the role of a key decision-maker. The simulation will change and branch based on the decisions students make, providing a variation of scenario paths. Upon completion of each simulation, students receive a grade, as well as a detailed report of the choices they made during the simulation and Decision Making the associated consequences of those decisions. Critical Thinking • Writing Space – better writers make great learners—who perform better in their courses. Providing a single location to develop and assess concept mastery and critical thinking, the Writing Space offers automatic graded, assisted graded, and create your own writing assignments, allowing you to exchange personalized feedback with students quickly and easily. -
Pervasive Data Analytics, Uncertainty and Policy Intervention in Disruptive Technology and Its Geographic Spread
International Journal of Geo-Information Concept Paper Taming Disruption? Pervasive Data Analytics, Uncertainty and Policy Intervention in Disruptive Technology and its Geographic Spread Roger C. Brackin 1,*, Michael J. Jackson 1, Andrew Leyshon 1 and Jeremy G. Morley 2 1 School of Geography University of Nottingham, Nottingham NG7 2RD, UK; [email protected] (M.J.J.); [email protected] (A.L.) 2 Ordnance Survey, Southamption SO16 0AS, UK; [email protected] * Correspondence: [email protected] Received: 25 November 2018; Accepted: 10 January 2019; Published: 16 January 2019 Abstract: The topic of technology development and its disruptive effects has been the subject of much debate over the last 20 years with numerous theories at both macro and micro scales offering potential models of technology progression and disruption. This paper focuses on how theories of technology progression may be integrated and considers whether suitable indicators of this progression and any subsequent disruptive effects might be derived, based on the use of big data analytic techniques. Given the magnitude of the economic, social, and political implications of many disruptive technologies, the ability to quantify disruptive change at the earliest possible stage could deliver major returns by reducing uncertainty, assisting public policy intervention, and managing the technology transition through disruption into deployment. However, determining when this stage has been reached is problematic because small random effects in the timing, direction of development, the availability of essential supportive technologies or “platform” technologies, market response or government policy can all result in failure of a technology, its form of adoption or optimality of implementation. -
Philosophy, Emergence, Crowdledge, and Science Education
Themes in Science & Technology Education, 8(2), 115-127, 2015 Big Data: Philosophy, emergence, crowdledge, and science education Renato P. dos Santos [email protected] PPGECIM- Doctoral Program in Science and Mathematics Education, ULBRA-Lutheran University of Brazil, Brazil Abstract. Big Data already passed out of hype, is now a field that deserves serious academic investigation, and natural scientists should also become familiar with Analytics. On the other hand, there is little empirical evidence that any science taught in school is helping people to lead happier, more prosperous, or more politically well-informed lives. In this work, we seek support in the Philosophy and Constructionism literatures to discuss the realm of the concepts of Big Data and its philosophy, the notions of ‘emergence’ and crowdledge, and how we see learning-with-Big-Data as a promising new way to learn Science. Keywords: Science education, philosophy of Big Data, learning-with-Big-Data, emergence, crowdledge Introduction The growing number of debates, discussions, and writings “demonstrates a pronounced lack of consensus about the definition, scope, and character of what falls within the purview of Big Data” (Ekbia et al., 2015). The usual ‘definition’ of Big Data as “datasets so large and complex that they become awkward to work with using on-hand database management tools” (Snijders, Matzat, & Reips, 2012) is unsatisfactory, if for no other reason than the circular problem of defining “big” with “large” (Floridi, 2012). In (dos Santos, 2016), we have already discussed a possible comprehensive definition of Big Data. Among the many available definitions of Big Data, we find the following one a most insightful for the purposes of this study: “Big data is more than simply a matter of size; it is an opportunity to find insights in new and emerging [emphasis added] types of data and content, to make your business more agile, and to answer questions that were previously considered beyond your reach” (IBM, 2011). -
The Next Big Thing We Won't Be Able to Live Without? Fulbright's Half-Life
2015 ASCUE Proceedings The Next Big Thing We Won’t Be Able to Live Without? Fulbright’s Half-Life Theory Gives Us Some Ideas Ron Fulbright University of South Carolina Upstate 800 University Way Spartanburg, SC 29303 (864)503-5683 [email protected] Abstract My great-grandparents lived one-half of their lives without electricity. My grandparents lived one-half of their lives without a telephone. My parents lived one-half of their lives without a television. My sister has lived one-half of her life without a computer and I have lived one-half of my life without Google. Today, we could not imagine life without these must-have technologies. With the current college student being about 20 years old, we ask ourselves what must-have technology will this generation live one-half of their lives without? Whatever it is currently is in research labs, will probably be an early product in the 2020-2025 time frame, and become a life-changing technology in 2030-2040. It will change the way we live, work, recreate, and will make billions of dollars. But, it won't be anything we know and love and use today. What are some of the possibilities? This subject yields wonderful in-class discussion in any college-level course and gives students a different way to perceive their place in history. Introduction Since the beginning of the industrial revolution, each generation has witnessed the advent and mass adoption of technologies we now view as indispensable to our daily lives. At some point in their lives the “must-have” technology was not available. -
Hr's Place in the Fourth Industrial Revolution
FACT SHEET · FEBRUARY 2020 1 FEBRUARY 2020 · NUMBER 2020/01 FACT SHEET HR’S PLACE IN THE FOURTH INDUSTRIAL REVOLUTION HR’S PLACE IN THE FOURTH INDUSTRIAL REVOLUTION FACT SHEET · FEBRUARY 2020 2 HOW TO READ AND INTERACT WITH THIS FACT SHEET This Fact Sheet is designed to reflect the inter-connectivity and layered paths of the fourth industrial revolution by the hyperlinks to multimedia resources and networks of information, juxtaposed textboxes and lines of thinking, and multimodal engagement of the reader. It is meant to be a medium for the reader to navigate and journey in-between the many resources available. INFORMATION This icon represents a small piece of extra information that can add to the context of a certain section, paragraph or point being made. External resources for the added benefit of the reader. This icon will be used in the format of EXTERNAL RESOURCES a button and will direct the reader through a link to a website with the relevant information. INSIGHT A small insight into the section, paragraph or point being made. The icon is used in the format of an indication and reference to a block with the relevant insight. CLICK HERE This icon gives the reader an opportunity to click on a button and has the reader taken to a space that the author would want to direct the readers attention. A video can be a great way to bring across an important point to the reader. This icon will be in VIDEO the format of a button that will transport the reader to a video through a web link. -
Research Cycles
FROM THE EDITOR « Research Cycles early all research goes in cycles, This was followed by another AI these, it was only recently that I saw it and I have already experienced “chill” in the 1980s [4], but the field is codified in the form of a “hype cycle” N this many times in my career. For now undergoing a resurgence given [8]. The typical curve, shown in Figure 1, example, when I started on the fac- the excitement about 1) self-driving ve- plots expectations (sometimes labeled ulty at Stanford in 1994, it was at the hicles (SDVs), building upon the many as visibility) versus time and has the end of the period of intense interest years of research into robotics and the characteristic shape of a transient re- in controls-structures interaction [1], control of embedded systems, and sponse of a lightly damped system to which had lasted the entire time I was 2) deep learning, which builds on the a step input, but with the longer tim- a graduate student and postdoctoral earlier work on neural networks and escale response dominated by that of candidate (a period of approximately reinforcement learning (see [5]–[7] for a system with a slow (stable) real pole. eight years). However, by the time the technical details and a historical per- With illustrative names, such as peak Shuttle experiment we designed on the spective on that approach). of inflated expectations, trough of disil- use of active control for space struc- There are also many new exciting lusionment, and plateau of productivity, tures flew on STS-67 in March 1995 [2], areas in the control field, such as the the curve shows the tendency for early that interest had waned significantly control of cyberphysical systems using expectations to become “overblown,“ and the funding agencies had largely techniques including linear and signal which, once realized, leads to negative moved on to different topics. -
Trends Driving the Next Wave of Technologies
Trends driving the next wave of technologies By Brian Burke Supporting technological innovation (Image credit: Shutterstock / carlos castilla) As technology innovation leaders, CTOs and CIOs progress through different stages of digital transformation much earlier than the rest of society. It is becoming imperative for their roles to stay up to date with emerging technologies that will impact their industries and create opportunities their organization can leverage for growth. Gartner’s Hype Cycle for Emerging Technologies explores the opportunities for organizations in their search for technology-enabled business transformation. This year’s Hype Cycle highlights 30 technology profiles that will significantly change society and business over the next five to 10 years. Present in each emerging technology are five drivers driving trends that, if harnessed in day-to-day business, will make sure your company is ready to ride the next wave of technology innovation. Trust in algorithms As the amount of consumer data increases rapidly, organizations have been exposed for implementing biased AI, sharing customers personal data, and distributing fake news. This has ultimately led to consumer distrust for central authorities within organizations when it comes to privacy issues, resulting in a shift from trusting organizations to trusting algorithms. Algorithmic trust models ensure the privacy and security of data, provenance of assets, and the identities of people and things. To start rebuilding trust with your customers, employees and partners, organizations should examine the following technologies: Secure access service edge (SASE) Differential privacy Authenticated provenance Bring your own identity (BYOI) Responsible AI Explainable AI For example, the use of blockchain technology is being applied for “authenticated provenance”, authenticating assets to ensure they’re not fake or counterfeit.