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Steve warburton

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Steve warburton

  1. 1. INNOVATION AND DISRUPTION IN HIGHER EDUCATION Managing change Steven Warburton, University of Surrey, UK. eLearning 2.0 2013 Brunel University
  2. 2. Technological change is exponential not linear. Knee of the curve (2014) „The Singularity Is Near‟ (Kurzweil, 2005)
  3. 3. Trends 2013: 1. Mobile Device Battles 2. Mobile Applications and HTML5 3. Personal Cloud 4. Enterprise App Stores 5. The Internet of Things 6. Hybrid IT and Cloud Computing 7. Strategic Big Data 8. Actionable Analytics 9. In Memory Computing 10. Integrated Ecosystems
  4. 4. “We are at the cutting edge of tradition”
  5. 5. the four stages of acceptance • Ignorance • Irrelevance • Important (but not for us) • I always told you so
  6. 6. Disruptive innovation • Sustaining: VLE (institutional control) • Disruptive: social media, open networks, OERs (leaner choice) -> MOOCs
  7. 7. 1. Relevance, value proposition and value network 2. Digital literacy, participation and exclusion 3. Sustainable delivery and business models 4. Demographic shift, life-long learning and linking formal and informal learning 5. Big data, privacy, data protection and digital identity Challenges University: 'a series of schools and departments held together by a central heating system’ (Robert Maynard Hutchins)
  8. 8. 1. Old Age Wellness Manager / Consultant 2. Vertical Farmer 3. Nano-Medic 4. Climate Change Reversal Specialist 5. New Scientists Ethicist ‘The shape of jobs to come: Possible New Careers Emerging from Advances in Science and Technology (2010 – 2030)’.Fast Future Research
  9. 9. • Volvo’s CEO suggests by 2025 a European deficit in engineers of 500,000 • Predictions in the ICT sector suggest a 2015 Europe- wide shortfall of 700,000 professionals Who to blame? • ‘Education is an obvious culprit’. • ‘Part of the problem is the time lag between curriculum development and the arrival of qualified graduates in the marketplace. ‘ Employment paradox: record youth unemployment levels BUT a massive skills shortage
  10. 10. • The cost of a 4-year college degree has increased y 2 to 3 times since the 80s • Starting salaries for graduate have remained flat in real terms • Universities vulnerable to disruptive innovation where easy-to-ignore “inferior,” low- cost alternatives improve to the point where they become a serious threat.
  11. 11. the avalanche? • MOOCs could lead HE into a ‘Napster’ moment’ - Martin Bean, Open University • President of Stanford University - "a digital tsunami", threatening to sweep aside conventional university education – Guardian article • 36 universities employing 36 academics each offer a first year mathematics course. The 36 universities collaborate and develop a single first-year mathematics course which is available to all students online and for free. • Do the universities need the 36 academics? • Does the government need 36 universities? • The answer to both questions, of course, is no.
  12. 12. MOOC platform, country Quoted student numbers (date of announcement) Number of courses (as of 28/5/13) Number of institutions Coursera, US 3,670,803 (on 28/05/13) 374 70 EdX, US 900,000 (approx, May 2013) 53 27 Udacity, US 400,000 (approx, May 2013) 25 1 FutureLearn, UK N/A N/A 21
  13. 13. MOOC learner types and proportions http://lytics.stanford.edu/deconstructing-disengagement/ “Deconstructing Disengagement: Analyzing Learner Subpopulations in Massive Open Online Courses,”
  14. 14. Michael Feldsteinhttp://mfeldstein.com/insight-on-mooc-student-types-from-eli- focus-session/
  15. 15. Edinburgh MOOCs headlines The results of a survey of 45,000 users: • A very high proportion of window-shopping learners in all MOOCs • 176 nationalities participated • Dramatic declines in participation from enrollment to Week 1 • Thereafter, continued participation varied widely between the six MOOCS • Main reasons given for joining the courses were: – Curiosity about MOOCs and online learning – Desire to learn new subject matter. • Career advancement and obtaining certificates were less important motivations • MOOC learners are more akin to lifelong learning students in traditional universities than to students on degree programmes http://www.era.lib.ed.ac.uk/bitstream/1842/6683/1/Edinburgh%20MOOCs%20Report%2 02013%20%231.pdf
  16. 16. • Three online for- credit math courses for $150 to 100 students per course; • Of those students, half were San Jose State students and the other half were un-enrolled students; • Students underperformed; • Lacked appropriate access; • Course put together in haste.
  17. 17. “The provosts of Big 10 universities and the University of Chicago are in high-level talks to create an online education network across their campuses, which collectively enroll more than 500,000 students a year. And these provosts from some of America’s top research universities have concluded that they – not corporate entrepreneurs and investors -- must drive online education efforts.” Controlling a disruptive innovation? https://www.documentcloud.org/documents/716121-cic-online- learning-collaboration-a-vision-and.html http://www.insidehighered.com/news/2013/06/19/big-10-provosts- question-partnerships-ed-tech-companies
  18. 18. A TIME OF CONVERGENCE? 5 THEMES
  19. 19. Online learning – the innovation space • “Some faculty members report that their online classes have been among the most exciting and creative teaching experiences of their careers. Many said it has reinvigorated their instruction, encouraging innovative strategies for reaching and teaching students. • Across the curriculum, the dichotomy between “traditional” and “online” offerings is breaking down, as a continuum of “blended” possibilities increasingly becomes the instructional norm across our campuses. • Many faculty and many students are finding enrichment in this period of rapid instructional innovation.” https://www.documentcloud.org/documents/716121-cic-online- learning-collaboration-a-vision-and.html
  20. 20. http://www.christenseninstitute.org/publications/hybrids/
  21. 21. 1. It includes both the old and new technology (whereas a pure disruption does not offer the old technology in its full form). 2. It targets existing customers, rather than nonconsumers—that is, those whose alternative to using the new technology is nothing at all. 3. It tries to do the job of the preexisting technology. As a result, the performance hurdle required to delight the existing customers is quite high because the hybrid must do the job at least as well as the incumbent product on its own, as judged by the original definition of performance. 4. It tends to be less “foolproof ” than a disruptive innovation. It does not significantly reduce the level of wealth and/or expertise needed to purchase and operate it. Hybrid innovation: four characteristics:
  22. 22. Competency-based, outcomes orientated learning • Learning = constant • Time = variable • Alignment of learning outcomes with job market requirements • Adaptive learning processes • Personalised – Individualised, differentiated, taking account of interest experience and preferences • Seven careers – constant engagment with learning
  23. 23. What you need to learn How you can learn Demonstrating your learning
  24. 24. Electronic learning progressions -> maps Electronic student portfoliosLearning positioning system
  25. 25. Source: Desire2Learn Analytics Engine
  26. 26. Rienties, B., Heliot, Y., & Jindal-Snape, D. (2013). Understanding social learning relations of international students in a large classroom using social network analysis. Higher Education. Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Educational Technology and Society, 11 (3), 224–238.
  27. 27. Type of Analytics Level or Object of Analysis Who Benefits? Learning Analytics Educational data mining Course-level: social networks, conceptual development, discourse analysis, “intelligent curriculum” Learners, faculty Departmental: predictive modeling, patterns of success/failure Learners, faculty Academic Analytics Institutional: learner profiles, performance of academics, knowledge flow Administrators, funders, marketing Regional (state/provincial): comparisons between systems Funders, administrators National and International National governments, education authorities Siemans, G. (2011) http://www.learninganalytics.net/?p=131
  28. 28. • Institution-specific toolboxs that enhance personal and organizational productivity. • A balance of tools and timing for the implementation of technology to create an ecosystem of technical capabilities • Enable synergies of cost-effective flexibility for the infrastructure, exostructure and the end user. Gartner, 2013
  29. 29. In conclusion
  30. 30. 'Disruptive innovation and the higher education ecosystem post-2012' Leadership Foundation Stimulus Paper
  31. 31. Strategic response? MOOC CORE ONLINE Business Innovation outwards inwards
  32. 32. MOOC Strategic partnerships?
  33. 33. Thank you
  34. 34. • Technology Development • The competitive advantages of MOOCs provided by UK HEIs or on UK platforms would be increased by a technological lead in the following areas. • Adaptive learning driven by learner analytics • Badging and Accreditation technology. This could be not merely about course content achievements, but also about learning-related skills such as reputational impact in social media. • Authentication technology (Retina, keystroke, challenge) which would leverage the robust and proven peer assessment methods
  35. 35. • MOOCs as a centre of innovation and pulling in many areas together: • lightweight accreditation e.g. badging • flipped classroom • analytics • adaptive learning • competency based learning • authentication • http://chronicle.com/article/The-Future-Is-Now-15/140479 • Innovations to impact on higher education over the next 36 months: • 1: e-Advising • 2: Evidence-based pedagogy • 3: The decline of the lone-eagle teaching approach • 4: Optimized class time (Stanford medical School: 70% formal education online) • 5: Easier educational transitions • 6: Fewer large lecture classes • 7: New frontiers for e-learning • 8: Personalized adaptive learning • 9: Increased competency-based and prior-learning credits • 10: Data-driven instruction • 11: Aggressive pursuit of new revenue • 12: Online and low-residency degrees at flagships • 13: More certificates and badges • 14: Free and open textbooks • 15: Public-private partnerships
  36. 36. Entrepreneurial manic-depression
  37. 37. 1. Body Part Maker 2. Nano-Medic 3. Pharmer of Genetically Engineered Crops and Livestock 4. Old Age Wellness Manager / Consultant Specialists 5. Memory Augmentation Surgeon 6. New Science‘ Ethicist 7. Space Pilots, Architects and Tour Guides 8. Vertical Farmers 9. Climate Change Reversal Specialist 10. Quarantine Enforcer 11. Weather Modification Police 12. Virtual Lawyer 13. Avatar Manager / Devotees - Virtual Teachers 14. Alternative Vehicle Developers 15. Narrowcasters 16. Waste Data Handler 17. Virtual Clutter Organizer 18. Time Broker / Time Bank Trader 19. Social 'Networking' Worker 20. Personal Branders
  38. 38. “Everyone designs who devises courses of action aimed at changing existing situations into preferred ones.”
  39. 39. 159...LIGHT ON TWO SIDES OF EVERY ROOM When they have a choice, people will always gravitate to those rooms which have light on two sides, and leave the rooms which are lit only from one side unused and empty. Therefore: Locate each room so that it has outdoor space outside it on at least two sides, and then place windows in these outdoor walls so that natural light falls into every room from more than one direction. (Alexander et al., 1977) Context: building an internal space for people
  40. 40. Problem Solution Context
  41. 41. ‘Each pattern describes a problem which occurs over and over again in our environment, and then describes the core of the solution to that problem, in such a way that you can use this solution a million times over, without ever doing it the same way twice.’
  42. 42. 1. Capture and re-use expert design knowledge 2. Establish common terminology and language 3. Provide the necessary level of abstraction for solving novel problems … encouraging creative as opp to drivative use.
  43. 43. Participatory pattern workshops
  44. 44. Participatory Pattern Workshops
  45. 45. Digital Identity Panic Facet MeLeaving Trails Putting Children First Permissioned Aggregation Purposeful Delay Space for Lurking What is My Name Digital Identity Pattern Collection at http://purl.org/planet/Main/ Personal Professional Technical Social Wear your skills Identity Placemaking Identity Before Collaboration
  46. 46. Data, data, everywhere • Stored information 2009 totaled 0.8 zetabytes (800 billion gigabytes). IDC predicts by 2020, 35 zetabytes of information will be stored globally. • Comprises messy data, such as social networks user profiles and posts … digital traces of our online transactions
  47. 47. Source: Cisco systems By 2011, twenty typical households were capable of generating more traffic than the entire Internet in 2008.
  48. 48. Three types of analytics intervention 1. Efficiency in the wider functioning of the institution (which has few implications for teaching practice); 2. Enhanced regulation of the teaching and learning environment (which has potentially negative impact on teaching practice); 3. Methods and tools intended to help lecturers carry out their tasks more effectively (which have the potential to be a useful tool in teaching practice). The Implications of Analytics for Teaching Practice in Higher Education Professor Dai Griffiths (IEC), JISC CETIS Analytics series, Vol. 1 Number 10.
  49. 49. Benefits: • Lead indicators and predictive models for identifying students that need additional learning support; • Reductions in student attrition, • Measurement of student graduate attributes • Development of scalable methods for enhancing teaching practice
  50. 50. Trends -> predications Reflection Learners Data Metrics Intervention … providing actionable insights e.g. remediation and acceleration of learning
  51. 51. • Analytics should not only to react to the present, but also to predict future trends and ‘respond’ accordingly. • Rio Salado University claims that they can predict with 70 per cent accuracy, and after 8 days of class, whether a student will score a ‘C’ or better. Parry, M. (2012). Big Data on Campus. The New York Times, 1–9.
  52. 52. But … big data poses big questions • Ethics • Legality* • Neutrality • Art of teaching versus the science of teaching • Objective versus subjective interpretation • Occularcentrism, representation becomes truth *http://www.technollama.co.uk/analysis-of-ukeu-law-on-data-mining-in-higher-education-institutions
  53. 53. What does analytics demand from us? • Analytics allow the educator and learner to access information that has previously been the domain of the researcher Therefore: • Data visualisation and data literacy are key; • Providing context to act on analytics information is a vital part of ‘closing the loop’.

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