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 http://lytics.stanford.edu/deconstructing-disengagement/ 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.
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?
1. INNOVATION AND DISRUPTION IN
Steven Warburton, University of Surrey, UK.
eLearning 2.0 2013
2. Technological change is exponential not linear.
Knee of the curve (2014)
„The Singularity Is Near‟ (Kurzweil, 2005)
3. Trends 2013:
1. Mobile Device Battles
2. Mobile Applications and
3. Personal Cloud
4. Enterprise App Stores
5. The Internet of Things
6. Hybrid IT and Cloud
7. Strategic Big Data
8. Actionable Analytics
9. In Memory Computing
10. Integrated Ecosystems
4. “We are at the cutting edge of tradition”
5. the four stages of acceptance
• Important (but not for us)
• I always told you so
6. Disruptive innovation
• Sustaining: VLE (institutional control)
• Disruptive: social media, open networks,
OERs (leaner choice) -> MOOCs
7. 1. Relevance, value proposition and
2. Digital literacy, participation and
3. Sustainable delivery and business
4. Demographic shift, life-long learning
and linking formal and informal
5. Big data, privacy, data protection
and digital identity
University: 'a series of schools and departments held together by a central heating system’ (Robert
8. 1. Old Age Wellness Manager / Consultant
2. Vertical Farmer
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. • 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. • 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
• Universities vulnerable to
disruptive innovation where
easy-to-ignore “inferior,” low-
cost alternatives improve to
the point where they become
a serious threat.
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. MOOC platform, country
numbers (date of
courses (as of
(approx, May 2013)
(approx, May 2013)
FutureLearn, UK N/A N/A 21
13. MOOC learner types and proportions
“Deconstructing Disengagement: Analyzing Learner
Subpopulations in Massive Open Online Courses,”
14. Michael Feldsteinhttp://mfeldstein.com/insight-on-mooc-student-types-from-eli-
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
• 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
• MOOC learners are more akin to lifelong learning students in
traditional universities than to students on degree programmes
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
• Lacked appropriate
• Course put together
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
Controlling a disruptive innovation?
18. A TIME OF CONVERGENCE?
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
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
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. Competency-based, outcomes
• Learning = constant
• Time = variable
• Alignment of learning outcomes with job market
• Adaptive learning processes
• Personalised – Individualised, differentiated, taking
account of interest experience and preferences
• Seven careers – constant engagment with learning
23. What you need to learn
How you can learn Demonstrating your learning
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. Type of Analytics Level or Object of Analysis Who Benefits?
Course-level: social networks,
discourse analysis, “intelligent
modeling, patterns of
Institutional: learner profiles,
performance of academics,
comparisons between systems
National and International National governments,
Siemans, G. (2011) http://www.learninganalytics.net/?p=131
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
29. In conclusion
30. 'Disruptive innovation and the higher education ecosystem post-2012'
Leadership Foundation Stimulus Paper
31. Strategic response?
33. Thank you
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. • MOOCs as a centre of innovation and pulling in many areas together:
• lightweight accreditation e.g. badging
• flipped classroom
• adaptive learning
• competency based learning
• 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. Entrepreneurial manic-depression
37. 1. Body Part Maker
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
16. Waste Data Handler
17. Virtual Clutter Organizer
18. Time Broker / Time Bank Trader
19. Social 'Networking' Worker
20. Personal Branders
38. “Everyone designs who
devises courses of action
aimed at changing existing
situations into preferred
39. 159...LIGHT ON TWO SIDES OF EVERY
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.
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
(Alexander et al., 1977)
Context: building an internal space for people
40. Problem Solution
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. 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. Participatory pattern
44. Participatory Pattern Workshops
45. Digital Identity Panic
Facet MeLeaving Trails
Putting Children First
Space for Lurking
What is My Name
Digital Identity Pattern Collection at http://purl.org/planet/Main/
Wear your skills
Identity Placemaking Identity Before Collaboration
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
• Comprises messy data, such as social
networks user profiles and posts … digital
traces of our online transactions
47. Source: Cisco systems
By 2011, twenty
were capable of
traffic than the entire
Internet in 2008.
48. Three types of analytics intervention
1. Efficiency in the wider functioning of the
institution (which has few implications for teaching
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.
• Lead indicators and predictive models for
identifying students that need additional
• Reductions in student attrition,
• Measurement of student graduate
• Development of scalable methods for
enhancing teaching practice
50. Trends ->
… providing actionable insights e.g. remediation and acceleration of learning
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. But … big data poses big questions
• Art of teaching versus the science of teaching
• Objective versus subjective interpretation
• Occularcentrism, representation becomes
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
• Data visualisation and data literacy are key;
• Providing context to act on analytics
information is a vital part of ‘closing the loop’.