Increasing Value Through Technology - Education Summit
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The document discusses the roles of technology in higher education. It explores how technology can enable more personalized and adaptive learning through tools like learning analytics and intelligent tutoring systems. However, higher education institutions face challenges in disrupting traditional models of education and achieving coherence in strategic technology adoption. Moving forward, technologies like quantum computing, artificial intelligence, and adaptive learning platforms may help create more individualized learning experiences through "technology patterning" for each student.
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Increasing Value Through Technology - Education Summit
- 1. The Mind Lab by Unitec | 2016 Increasing value through technology Higher Education Summit 2016
- 2. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 Technology in learning and teaching? Evidence has partial influence upon practice with practitioners preferring to consult colleagues and academic developers (Price & Kirkwood, 2014) Lifelong Learning? A lifelong learning perspective can help the higher education workforce to adapt while acting as ‘professional role models’ to students (Kukulska-Hulme, 2012) Strategy? From an ecosystems institutionalization point of 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. (Vargo, Wieland & Archpru Akaka, 2014) What are the roles of technology in higher education?
- 3. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 “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, p.11) 1)Law of accelerating returns
- 4. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 Not breakthrough innovation Existing customers in back plane cannot use it Simplicity and affordability targets ‘non-consumers’ New plane of competition Displaces incumbents (Christensen, Horn & Johnson, 2008, p. 47). 2) Disruptive Innovation
- 5. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 2009-2010: Uber in SF Bay area March 2016: Uber seeks autonomous fleet - operational by 2025-2030 June 2016: Local Motors (Arizona) - autonomous 3D printed bus (12 people) that talks to passengers (10 hrs to print and 1 hr to assemble) Industries and disruption https://nz.pinterest.com/pin/341218109248254640/
- 6. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 MOOCS - owning the HE platform?
- 7. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 Why have incumbents in higher education not been displaced? “In the past, teaching was difficult to disrupt because its human qualities couldn’t be replicated… we observed two distinct groups of college students who have different “jobs-to-be-done”. higher education has seen many new entrants but few exits: alumni and state legislators are ‘customers’ of their institutions. Their support is typically driven not only by public spiritedness but also by deep personal relationships with faculty members and coaches who profoundly moulded their lives” (Christensen & Eyring, 2011, p. 411). Disruption in higher education?
- 8. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 NMC Horizon Report (HE) 2016 Timeframe Trends Developments Challenges 1 - 2 years ● Measuring learning ● Blended learning designs ● Bring your own device ● Learning analytics / adaptive learning Solvable: Blending formal & informal learning Improving digital literacy 3 - 5 years ● Redesigning learning spaces ● Deeper learning approaches ● Augmented / virtual reality ● Makerspaces Difficult: Competing models of education Personalizing learning 5+ years ● Advancing cultures of innovation ● Rethinking how institutions work ● Affective computing ● Robotics Wicked: Balancing connected and unconnected lives Keeping education relevant 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.
- 9. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 “There will be one big transformation: viable measures of comparative student learning outcomes, including value added between enrolment and graduation. These measures will be as revolutionary in their effects as global research rankings have been.“ Simon Marginson is professor of international higher education at the UCL Institute of Education, and director of the Economic and Social Research Council/Higher Education Funding Council for England Centre for Global Higher Education. Retrieved from: https://www.timeshighereducation.com/features/what-will-universities-look-like-in-2030-future-perfect Measuring learning
- 10. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 “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. ‘Learning analytics and adaptive learning’
- 11. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 Case studies suggest: A tool for quality assurance and quality improvement A tool for boosting retention rates A tool for assessing and acting upon differential outcomes among the student population An enabler for the development and introduction of adaptive learning JISC: Learning analytics Sclater, N., Peasgood, A. & Mullan, J. (2016). Learning Analytics in Higher Education. A review of UK and International best practice. Retrieved from https://www.jisc.ac.uk/sites/default/files/learning-analytics- in-he-v3.pdf
- 12. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 Next generation ‘intelligent tutoring’? http://futurism.com/artificially-intelligent-lawyer-ross-hired-first-official-law-firm/
- 13. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 Quantum computing http://www.dwavesys.com/sites/default/files/D-Wave%202X%20Tech%20Collateral_0915F.pdf
- 14. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 ‘Technology patterning’ for a cohort of ONE Quantum computing (like D Wave 2X) + Artificial intelligence (like IBM Watson) + MOOCS (like EdX) + Pervasive Learning Analytics and Adaptive Learning = Next generation intelligent tutoring systems
- 15. The Mind Lab by Unitec | 2016The Mind Lab by Unitec | 2016 “Coherence consists of the shared depth of understanding about the purpose and nature of the work” (Fullan & Quinn, 2016, p.1) Significantly improved learning in an entire province (Ontario) - across 5, 000 schools (Fullan & Quinn, 2016) The case of Harvard DNA and BYU-Idaho: identify and pursue those things you can do uniquely well (Christensen & Eyring, 2011) ‘Initiativitis’ vs Coherence (Fullan & Quinn, 2016, p.12)
- 16. The Mind Lab by Unitec | 2016 References Christensen, Clayton M., Horn, Michael B., and Johnson, Curtis W. (2008). Disrupting class. How disruptive innovation will change the way the world learns. New York: McGraw Hill. Christensen, Clayton M. & Eyring, Henry, J. (2011). The innovative university: changing the DNA of higher education from the inside out [Kindle edition]. San Francisco: Jossey- Bass. Fullan, M. & Quinn, J. (2016). Coherence. The right drivers in action for Schools, Districts and Systems. Thousand Oaks: Corwin. 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. 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. Kurzweil, Ray. 2005. The singularity is near: when humans transcend biology. New York: Viking. Kurzweil, R. (2013). How to create a mind – the secret of human thought revealed [Kindle Edition]. New York: Penguin. Marshall, S. (2010). Change, technology and higher education: are universities capable of organizational change? ALT-J: Research in Learning Technology, 18(3), 179 - 192. Price, L. & Kirkwood, A. (2014). Higher Education Research & Development, 33 (3), 549–564. doi: 10.1080/07294360.2013.841643 Vargo, Stephen L., Heiko Wieland, and Melissa Archpru Akaka. (2015). Innovation through institutionalization: A service ecosystems perspective. Industrial Marketing Management 44, 63-72.
- 17. The Mind Lab by Unitec | 2016 herbert@themindlab.com Thank you
Editor's Notes
- 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.
- 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).
- 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).
- 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
- 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.
- 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.