Steve warburton


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  • The world no longer appears infinite in resources, slow paced, linear and stable. We now see the limitations; feel the impact of rapid change; and we can conceptualize the non-linear and unstable nature of it all! The idea is that this technological singularity is approaching, and we're not talking about 500 years from now… In his latest book called The Singularity Is Near, Ray Kurzweil claims it will happen probably around 2014, which makes also sense with many prophecies... I don't want to go new age here, but it's a fact that the Hopi and the Mayan too, set the end of their calendars on 21st of December 2012... plus, Terence McKenna (r.i.p.) with his Novelty theory found out something similar and in a much more "psychedelic" way... he found out everything is accelerating at an increasing pace, and also found out that in 21st of December 2012 something will profoundly change the way we experience reality and time.The difference with Ray Kurzweil is Ray is a famous and trusted scientist that can release a well written book, easy to read, full of examples. Terence also was easy to read, wrote well written book, and provided examples, but was not famous and most of all not trusted by the scientific community. Kurzweil definitely is.But I don't want to digress too much.In his book, Kurzweil uses lots of data and examples to convince the reader about how "everything" (what McKenna called Novelty and what kurzweil might call Pattern Complexity) is accelerating at an increasing (exponential) rate. He states that in 2014 the acceleration will be visible and our old schemes for forecasting the future will not apply anymore.If at the moment we are probably just starting to get the feeling that something "isn't right", that things are flowing too fast, well, we better sit tight because its' just the beginning.Any exponential curve starts very slowly, not-distinguishable from a linear progression, as you cans see from this picture here.But at a certain point, called the "knee of the curve", there is a dramatic acceleration. Kurzweil states we are reaching the knee, we should get there by 2014. At that moment, we should be able to see the singularity, and all those old old human schemes should, finally and hopefully, brake down.The technological singularity is an AI (artificial intelligence) probably linked with our biological brain.Pure intelligence.Bio-machines pushing forward all boundaries.An intelligence far greater than ours.I guess all this could begin with the invention of a machine that can pass the Turing test.Kurzweil goes as far as speculating that this technological singularity will spread from earth to the whole universe. If speed of light can be surpassed it might happen very very fast.
  • How do we manage these – other than jumping on the MOOC bandwagon?
  • What are MOOCs? A sustaining or disruptive innovation … are institutions leaping onto the MOOC bandwagon as a response to exerting control over openness. The brutal As recently as 1976, the company held a 90% market share of film sales and 85% of camera sales. It was the Google of its day, attracting the best technical talent from across the country.Kodak’s primary strategy was to sell high margin film. Known as the razor blade strategy, the company developed inexpensive cameras as a means to an end: their purpose was to facilitate lucrative film sales. In summary, its digital camera invention was held back because of management’s concerns about the negative impact on film sales.In contrast, firms that set up separate subsidiaries have been able to grow game-changing innovations into full-fledged businesses. HP did this with the invention of the ink jet printer in the 1980s. It set up an autonomous subsidiary in Vancouver, Washington, far removed from the influence of corporate headquarters in Palo Alto, California. Initially, the ink jet printer market was small and limited; over time, the company turned it into a significant business.
  • See also – an ‘avalanche is coming’. So we need the TOOLS to deal with these challenges. For me me these tools are going to be based arounddesign and modeling.
  • Stanford’s Lytics Lab approached the problem by investigating and categorizing learners through courseware analytics, to reveal more granularity in the large populations dropping out. The report “Deconstructing Disengagement: Analyzing Learner Subpopulations in Massive Open Online Courses,” identified four significant clusters of students in three computer science MOOCs“Auditing” learners watched lectures throughout the course, but attempted very few assessments.“Completing” learners attempted most of the assessments offered in the course.“Disengaging” learners attempted assessments at the beginning of the course but then sometimes only watched lectures or disappeared entirely from the course.“Sampling” learners briefly explored the course by watching a few videos. Different distributions were noted across the three courses analysed (a High School, Undergraduate and Graduate School course, respectively ). at I think this sort of classification is really helpful and one (or more?) of them needs to feature in the Exec Summary Is there any further discussion abt why these profiles hold? It may be useful from the FE perspective.
  • Michael Feldstein’s chart aggregating the available data shows a characteristic distribution of MOOC learner types across course duration.
  • Practically what does this mean in the institution.A SNAPSHOT from our direction.Don’t forget compass point.Visualisation anddashboarding
  • Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes. SNA provides both a visual and a mathematical analysis of human relationships. Management consultants use this methodology with their business clients and call it Organizational Network Analysis [ONA].Dawson, S. (2009). ‘Seeing’ the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5), 736-752. A network diagram is a visual depiction of all interactions occurring among students and staff. This information provides rapid identification of the levels of engagement and network density emerging from any implemented online learning activities. Social network visualisations provide a snapshot of who is communicating with whom and to what level. A network diagram of your students’ discussions online can:• identify disconnected (at risk) students;• identify key information brokers within your class;• identify potentially high and low performing students so you can plan interventions before you even mark their work;• indicate the extent to which a learning community is developing in your class;• provide you with a “before and after” snapshot of what kinds of interactions happened before and after you intervened/changed your learning activity design (useful to see what effect your changes have had on student interactions and for demonstrating reflective teaching practice e.g. through a teaching portfolio)• allow your students to benchmark their performance without the need for marking.
  • Appropriated ideas from business intelligence and business modeling. Macro level, meso-level and micro-level
  • Supporting educational missions. Taking as a model the way news publishers have supported their titles through online versions, the report cites evidence that open educational resources drive up paid course enrollments by around 10%. Market leaders are particularly well placed to attract new students from a strong free web presence. Expanding a University’s footprint in the digital space is less risky, less costly and faster than doing so in the real world.  Driving Internationalisation. Noting that international participation in MOOCs is much higher than other forms of HE, Universities UK recommends MOOCs both as a lower cost alternative to some Trans National Education (TNE) arrangements, and as a way of delivering preparation and induction to students prior to embarking on TNE arrangements Diversified learning pathways. MOOCs offer a new and flexible pathway in learning. To take advantage, faculties are urged to join MOOCs experimentally, along with their students; and to audit the IP of their content with a view to clearing copyright for MOOC publication in due course.  Cost restructuring. Citing again the experience of news publishers, whose cost model has been re-invented by the internet, UniversitiesUK advises that the free MOOC model of online learning will impact the tuition-based revenue model. This will happen in four domains: Content production, building of delivery platforms, provision of feedback and support, and awards. In all these dimensions, MOOCs offer not just new financial models, but in addition the opportunity to unbundle elements of the HE package and deliver them through different channels Shared Services. Universities are also advised to embrace the possibilities of sharing content and student support services that come with aggregation platforms. The University of California move to provide a single statewide version of core courses shared by multiple instititions is offered as an example Learning R&D. UniversitiesUK underlines that MOOCs foster a set of so-called “emergent learning technologies” (as opposed to those already resident in installed Learning Management Systems) and highlights the potential from them. The priority technologies are listed as: Learning Analytics (to improve feedback to students)Adaptive Learning (personalized pathways)Social Network Analysis (puts connection and linkage to the fore)Discourse Analytics (automated assessment)Semantic Web Technologies (automated personalized enabling of customized support and content feeds)Virtual Problem Based Learning (immersive environments to hone procedural skills)The report makes the observation, based on the experience of Amazon and Google, that early adoption of such technologies can help to build dominant market positions, and applies this principle to Universities. (By way of commentary on this list, the present authors note that Universities UK have not identified the authentication technologies (Iris recognition, key stroke pattern) that would automate learning validation – we consider this an omission, especially as Coursera is already deploying some of them.) Reforming the Core. Under this rubric, UniversitiesUK considers a basket of institution-wide issues raised by the increasing role of online provision in University offers. Recommending that Universities rebalance “different aspects of an institution’s work, including online and physical, free and paid-for provision”, the report lists several areas of HE activity which may need to be reassessed including quality assessment, assurance and organizational structure. Included in this heading is the need to deliver courses that give students the skills to extract value from online environments – including new presentation and networking skillsets. Academics and administrators too will need re-educated, to exploit data and technology opportunities more effectively as part of their workload.
  • Capture and communicate successful practice as design knowledge.Describe – so what does a pattern look like.Everyone designs who devises courses of action aimed at changing existing situations into preferred ones
  • Timeless … and vene if we simply look at this pattern it deals so beautifully with technology.
  • My book.
  • Data mined via simultaneous search queries using natural language processing, machine learning, and software architectures (e.g. Hadoop)
  • Macro, meso, micro distinction.Politics of data, ethics.
  • Remediation and acceleration of learning is at the heart of a personalised learning experience.
  • MEQ, NSS, iGrad survey’s/ League tables and league positions.
  • It’s a clever system in several ways (i) the underpinning technology to allow recording screen capture at multiple levels, in built real time support mechanism – not dissimilar to a call-centre and finally the ability to capture real-time data both active and passive. What we are starting to do is now model the CAD classroom setting and the first part of this has been to look at the passive data – group position, attendance, time on task, We are finding strong correlation here between performance and group position.
  • What eye we cast and what algorithm we program.Where can we positoin ourselves within the landscape?
  • 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 “Deconstructing Disengagement: Analyzing Learner Subpopulations in Massive Open Online Courses,”
    14. 14. Michael Feldstein 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 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? learning-collaboration-a-vision-and.html question-partnerships-ed-tech-companies
    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.” learning-collaboration-a-vision-and.html
    20. 20.
    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)
    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 • • 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 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 *
    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’.