Increasing Value Through Technology - Education Summit

Editor's Notes

• #3: The use of technology for teaching and learning is now widespread, but its educational effectiveness is still open to question. This mixed-method study explores educational practices with technology in higher education. It examines what forms of evidence (if any) have influenced teachers’ practices. .. Findings suggest that evidence has partial influence upon practice with practitioners preferring to consult colleagues and academic developers. The study underscored the difficulty in defining and evaluating evidence, highlighting ontological and epistemological issues. The academic developer’s role appears to be key in mediating evidence for practitioners. Price, L. & Kirkwood, A. (2014). Higher Education Research & Development, 33 (3), 549–564. doi: 10.1080/07294360.2013.841643 In a time of change, higher education is in the position of having to adapt to external conditions created by widespread adoption of popular technologies such as social media, social networking services and mobile devices. For faculty members, there must be opportunities for concrete experiences capable of generating a personal conviction that a given technology is worth using and an understanding of the contexts in which it is best used. The paper examines approaches to educational professional development at The Open University, including recent initiatives related to faculty development in relation to mobile learning. The paper reflects on what can be learnt from these experiences and proposes a lifelong learning perspective which can help the higher education workforce to adapt. Faculty members have to commit to lifelong learning, remembering that ‘professional role model’ to students is one of the main roles of the teacher. Kukulska-Hulme, A. (2012). How should the higher education workforce adapt to advancements in technology for learning and teaching? Internet and Higher Education, 15, 247-254. This article explores the role of institutions ininnovation from a service- cosystems perspective, which helps to unify diverging views on innovation and extend the research regarding innovation systems. Drawing on institutional theories, this approach broadens the scope of innovation beyond firm-centered production activities and collaboration networks, and emphasizes the social practices and processes that drive value creation and, more specifically, innovation— the combinatorial evolution of new, useful knowledge. Based on this ecosystems view, we argue for institutionalization– the maintenance, disruption and change of institutions– as a central process of innovation for both technology and markets. In this view, technology is conceptualized as potentially useful knowledge, or a value proposition, which is both an outcome and a medium of value co-creation and innovation. Market innovation, then, is driven by the combinatorial evolution of value propositions and the emergence and institutionalization of new solutions. Vargo, Stephen L., Heiko Wieland, and Melissa Archpru Akaka. (2015). Innovation through institutionalization: A service ecosystems perspective. Industrial Marketing Management 44, 63-72.
• #4: My core thesis, which I call the law of accelerating returns (LOAR), is that fundamental measures of information technology follow predictable and exponential trajectories, belying the conventional wisdom that “you can’t predict the future” …There are still many things - …. – that remain unknowable, but the underlying price/ performance and capacity of information has nonetheless proven to be remarkably predictable (Kurzweil, 2013, Ch. 10, para. 1). The power (price-performance, speed, capacity, and bandwidth) of information technologies is growing exponentially at an even faster pace, now doubling about every year (Kurzweil, 2005: 25). For information technologies, there is a second level of exponential growth: that is, exponential growth in the rate of exponential growth (Kurzweil, 2005: 25). To express this another way, we won’t experience one hundred years of technological advance in the twenty-first century; we will witness on the order of twenty thousand years of progress (again, when measured by today’s rate of progress), or about one thousand times greater than what was achieved in the twentieth century (Kurzweil, 2005: 11).
• #5: A disruptive innovation is not a breakthrough improvement. Instead of sustaining the traditional improvement trajectory in the established plane of competition, it disrupts that trajectory by bringing to the market a product or service that actually is not as good as what companies historically had been selling. Because it is not as good, the existing customers in the back plane in Figure 2.1 cannot use it. But by making the product affordable and simple to use, the disruptive innovation benefits the people who had been unable to consume the back-plane product – people we call “non-consumers”. Disruptive innovations take root in simple, undemanding applications in what, as depicted in the front of Figure 2.1, is a new plane of competition – where the very definition of what constitutes quality, and therefore what improvement means, is different from what quality and improvement meant in the back plane (Christensen, Horn & Johnson, 2008: 47). In the years when the companies must commit to the innovation, disruptions are unattractive to the leaders because their best customers can’t use them, and they promise lower profit margins… One factor that makes it so hard for the incumbent leader to pursue a disruptive innovation is that the way the product performance is defined in the disruptive market is antithetical to the sorts of improvements that are required to succeed in the original market (Christensen, Horn & Johnson, 2008: 50-51).
• #9: Horizon Report on Higher Ed 2016 Key trends accelerating technology adoption in higher education Long-Term Impact Trends: Driving Ed Tech adoption in higher education for five or more years · Advancing Cultures of Innovation 8 · Rethinking How Institutions Work 10 Mid-Term Impact Trends: Driving Ed Tech adoption in higher education for three to five years · Redesigning Learning Spaces 12 · Shift to Deeper Learning Approaches 14 Short-Term Impact Trends: Driving Ed Tech adoption in higher education for the next one to two years · Growing Focus on Measuring Learning 16 · Increasing Use of Blended Learning Designs Significant Challenges Impeding Technology Adoption in Higher Education Solvable Challenges: Those that we understand and know how to solve · Blending Formal and Informal Learning 22 · Improving Digital Literacy 24 Difficult Challenges: Those that we understand but for which solutions are elusive · Competing Models of Education 26 · Personalizing Learning 28 Wicked Challenges: Those that are complex to even define, much less address · Balancing Our Connected and Unconnected Lives 30 · Keeping Education Relevant Important Developments in Educational Technology for Higher Education 34 Time-to-Adoption Horizon: One Year or Less · Bring Your Own Device (BYOD) 36 · Learning Analytics and Adaptive Learning 38 Time-to-Adoption Horizon: Two to Three Years · Augmented and Virtual Reality 40 · Makerspaces 42 Time-to-Adoption Horizon: Four to Five Years · Affective Computing 44 · Robotics
• #11: Learning analytics is an educational application of web analytics aimed at learner profiling, a process of gathering and analyzing details of individual student interactions in online learning activities. The goal is to build better pedagogies, empower active learning, target at-risk student populations, and assess factors affecting completion and student success. Adaptive learning technologies apply learning analytics through software and online platforms, adjusting to individual students’ needs. A paper by Tyton Partners describes adaptive learning as a “sophisticated, data-driven, and in some cases, nonlinear approach to instruction and remediation, adjusting to a learner’s interactions and demonstrated performance level, and subsequently anticipating what types of content and resources learners need at a specific point in time to make progress.” In this sense, contemporary educational tools are now capable of learning the way people learn. Enabled by machine learning technologies, they can adapt to each student in real time. Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., and Hall, C. (2016). NMC Horizon Report: 2016 Higher Education Edition. Austin, Texas: The New Media Consortium.
• #16: Technology and change are so closely related that the use of the word innovation seems synonymous with technology in many contexts, including that of higher education. This paper contends that university culture and existing capability constrain such innovation and to a large extent determine the nature and extent of organisational change. In the absence of strong leadership, technologies are simply used as vehicles to enable changes that are already intended or which reinforce the current identity. These contentions are supported by evidence from e-learning benchmarking activities carried out over the past five years in universities in the United States, the United Kingdom, Australia and New Zealand. [ABSTRACT] Marshall, S. (2010). Change, technology and higher education: are universities capable of organizational change? ALT-J: Research in Learning Technology, 18(3), 179 - 192.