Thanks for opportunity VICE Chancellor DaviesProfessors Howard Liu Brief bio / personal trajectory Dialogue
Unbundling Disaggregation Results of convergence of financial, demographic, technological, etc forces Recently referenced A Path Forward Game-Changing Reforms in Higher Education and the Implications for Business and Financing Models David Bergenron, Center for American Progress 1965 – Higher Education Act – Lyndon Johnson enshrined 30 week academic calendarGEDFinancial Aid formulasAcademic workload on 12 credit scale MP Staton among others, writing about current shift away from bundled, woven, systems
Forces driving disaggregation Globalized biz models – need for creating creators ( Friedman)Supply chains shift; production; design not to fill existing job but to create new ones Widening diversity in choice – where and how to be credentialedInequity US, the share of households earning middle-class income has declined from 50 per cent in 1970to 42 per cent in 2010. Youth un and under emoloymentCosts – cost out pacing returns 2000/01 and 2010/11, prices for undergraduate tuition, room and board at public institutions rose 42%, and prices at private, not-for-profit institutions rose 31% after adjustment for inflation the average earnings for US students with a bachelors degree fell 14.7 per cent between 2000 and 2012 despite a 72 per cent increase in cost.
Traditional, established, proven, roles of universities are not disappearing, but are migrating away from centralized institutional control Learner / consumer driven (akin to music, social relations, shopping etc)
Now migrating to differentiated, untested, unsupported , but ubiquitous the lack of clear pathways for students to take them through postsecondary education to a career. New models Stackable credentials – Complete College America Competency based credentialing Also driven by employer demands But employers also consistently identify a set of key skills or competencies such as critical thinkingcomplex problem solvingwritten and oral communicationapplied knowledge in real-world settings that do not necessarily align with a specific major
Things that thought looked a certain way, are now different both in appearance and in experience.
Given the unraveling / disaggregation, a way forward can be developed by examining several core threads / trends which overlap institutions and provide beginnings of an agenda Platforms – Delivery Classrooms – Teaching Analytics- ResearchDesign – Design
Will the current proliferation and adaption of new LMS platforms lead to deeper and sustained learning? Or As a consumer / developer of systems, what should be considered?
Where is teaching and learning in all of this? Robot classrooms? What is the new classroom? Teacher? Student? Is it a simple flip?
We are data crazedBig Data dominated from ….. to Edward Snowden. What is educational big data? How will it harvested, used? Can we see the road through our dashboard? My Austin texas cab driver
Are our existing ID models adequate to the task? Have our toolsets, platforms, and systems outpaced our ability to create and deliver a compelling, sticky learning experience? Are we wedded to land based design, when we need screen based definitions?
And as we understand the unraveling, what is the path forward? Where are the opportunities?
Increasing differentiationLegacy systems – Bb. D2L, MoodleArrival of Open Source Arrival of MOOC, assessment driven platforms SaaS concept
Edx claim to fameWas this about teaching / learning? Software as a Service? Marketing / branding?
Gartner hype cycle Classic tech trigger – where would MOOCs and related tech triggers be? What has been learned?
Despite emergence and hype of MOOCs, LMS market share still somewhat predictable Skewed by regionOncoming of Canvas, open source, product and UX driven
Another view – George Kroner, U Maryland
Out of this, unavoidable and expected shifts Away from singular product definition and deployment, and towards more nuanced, user experience driven matrix Much heralded and of the LMS is premature. CANVAS enterprise-level, open source and cloud-native LMS that doesn’t take six months to integrate, avoids Flash, while offering mobile support, analytics, integration with popular apps, speed grading, a “guaranteed uptime Service Agreement,” hands-free updates and giving developers a set of APIs and scalable server capacity — for in-person and online courses.Canvas Network
Considerations # of MOOC partner institutions divided by their LMS of choiceLearning Technologies Interoperability – IMS Global Learning Analytics Long/ SiemansMassive Open Online Courses (MOOCs), which occur in decentralized, distributed teaching and learning networks, offer another challenge. Online social media monitoring tools (e.g., Radian6) and reputation or influence monitoring tools (e.g., Klout) may provide educators with a model for analytics in such networks, in which activity is distributed across multiple sites and multiple identities.
The challenge of improving the classroom We’ve all been there, so we’re all experts. What if…… Popular conceptions of the future of teaching, but from the past 1910 – Meat Grinder Classroom Passive, centralized, equal (within), measurable, reliable
1958 Flipped Classroom Mechanical tabulations“gearing” , advance based on abilities – sounds like personalized learning Machine driven attendance
1968 Compressed speech Speed and ‘BETTER’
2012 – New coin of the realm is the flipped classroom or blended learning. Important to recall that the concept derived from CO hs teachers and not Kahn, remains largely untested. Reduce costsImprove retention? Lower attrition?
Discussion has been going on for some time BEFORE Kahn Academy
Add CT notes OLI / Stanford
Popular You tube channel – will it blend? Absolute requirement to assess and plan for faculty development and support in any flipped or blended effortIdentify instructional goals alongside operational goalsAnticipate hidden costs of production, deployment, researchIdentify core research agenda. Why do this?
Several models to considerFixed factors – devices etcOpen factorsaccountability and cheating Credentialing and certification; credit optionsInvestment and price points; credit hour costs? Gains in f2f time. Costs for applied learning labs or studios?
Data minefield PrivacyBusiness models - freemium to premium; who controls access? Whose Data? Is the end game the development of solutions to reinvent education or are there other goals to be met? Can personalized learning inform graduate and professional education? Are competency models viable?
Hurricane Sandy A revolution in data collection AND representation. Concept that source content is always fluid, Google Earth in constant update model; your Pandora station constantly changing; accelerated by dominance of cloud based computing. Not a document but an interaction; a software performance. Lev Manovich – Algorithms of Our Lives State of permanent change Welcome to the world of permanent change – a world defined not by heavy industrial machines that are modified infrequently but by software that is always in fluxNew LMS platforms are, by design, mutli-tier software, API packagesGoogle: Data Game Changers: SpeedSizeSensors
Data is now also about SCALEAnd learning data is ripe for a revolution Enlargement of Instagram project – each tile as an instagram user
Phototrails project, comparing Instagram usage by Tokyo, New York, Bangkok and San Francisco Software app used the same; results showing generally consistent patterns of use across cities, but with some hue differences
Siemans / LongAccording to the 1st International Conference on Learning Analytics and Knowledge, “learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.”11 “Analytics marries large data sets, statistical techniques, and predictive modeling. It could be thought of as the practice of mining institutional data to produce ‘actionable intelligence.’”12
Context of most recent ID deployments stems from traditional, land based learning theory. Not invalid, but needs significant adjustment to screen based learning environments. Even more pronounced by new Machine Learning and AI systems, abilities to create learning environments based on sophisticated software algorithms
Emphasize an evolving discipline – Need for new model of ID which incorporates cognitive science, learning theory, machine learning software toolsNeed for production and design models based on data driven, team iterations – plan, deploy, gather, iterate – evolving cycles. AGILE Opportunities to reference growing sophistication of learning analytics – towards a predictive, and personalized design. One size need not fit all
UN Medical Center
The Great Unraveling –
Thoughts on the Changing
Howard Lurie / January
University of Nebraska Medical Center / January 13 – 14, 20
CS4Ed – Consulting Services for Education
Emerging globalized business models
Widening faultlines in income, access and
Rapid escalation of higher education costs
Persistent decline in value of undergraduate and
Unfettered access to ubiquitous content
The city on a hill has company; increased
Forces Driving Disaggregation // Unbundling // Unraveling
and functions of
An Unbundling Framework
edX: “We‟ve Flipped the Funnel”
Took the Final
Passed the Mid-Term
Made it the Mid-Term
Tried the First Problem Set
Registered for 6.002x: Circuits and Electronics
Same staff resources as 150 person on-campus
Next Wave LMS
• Simple course delivery “learning
• Single course experience differentiated
pathways for acceleration or remediation
• Click through data predictive learner
analytics, consumable by both student and
• Digital content repositories stackable
content available across platforms and
• Departmentalized courses evolving and
practicum focused learning communities
• Course in a box mutli platform and
Next Wave LMS Progressions
Questions to Consider in LMS Determination
What is the adoption strategy? Single, multiple, or local?
What is the desired level of interoperability? LTI friendly?
Open? Closed Open? Open Open? Closed Closed?
How will courses be developed, and by whom? Support
• How will core instructional goals be met by an LMS or set of
Door Number One, Two, Three, or None?
Tomorrow’s schools will be more crowded; teachers will be correspondingly
fewer. Teaching would be by means of sound movies and mechanical
tabulating machines. Pupils would record attendance and answer questions
by pushing buttons. Special machines would be “geared” for each individual
student so he could advance as rapidly as his abilities warranted. Progress
records, also kept by machine, would be periodically reviewed by skilled
teachers, and personal help would be available when necessary.
Candace Thille, Schools of Tomorrow, 9/17/13
That‟s what I‟ve spent the last 10 years of my
life doing. One thing we found is that learning
is really complex…
Once a colleague asked me, „why do you study
learning? We all teach, it‟s not rocket science.‟
Well, actually it’s more complex than rocket
science. Really understanding human
learning at that episodic moment where you
have change in thought is a complex
Thille - More complex than rocket science…
Questions to Consider for Blended
• What are the support systems and
training resources for faculty and
production units to create and
deliver blended course content?
• What is the blended learning
research agenda? How will data
be collected and analyzed? How
will data inform the course
• What will be the associated business requirements? Credit
hours? Faculty compensation? Activity or platform fees?
Will it Blend?
More than Rocket Science…
• Investigate the “learning engineers” framework
• Anticipate faculty development and support; acknowledge
the destabilizing realities of blended learning.
• Develop instructional
solutions for instructional
• Demand a rigorous but
attainable research agenda;
contribute findings to a
Yes, it will blend, but…
Learning in the
Research and Practice in Assessment, Summer 2
How does learner activity correlate to performance?
Courtesy Seaton, Pritchard Aug 2012
Initial edX attempts at learner analytics
154k 108k “participants” in total time
Time/Week per Activity
Weekly time spent per activity
Arrow thickness ~ #
Resource area ~ time spent
What did “learners” use to learn?
• Weekends rule
• Global Procrastination
“Generally, what you see is learning analytics applied to
course management systems and course management
systems is basically just a fancy way of saying an online
discussion board and they are looking at how many times a
student will respond, how - to a discussion - how quickly they
respond to a discussion, how often they log on. So just very,
very basic levels of data that they're looking at, and so the
predictive models are not as accurate as they could be”.
Early Gap Analysis on Learning Data
Considerations for Predictive
• Design and build an
framework which includes
recruitment, retention, and
• Early articulation of faculty, student and research
requirements for data sets, indicators, and dashboards.
• Define the desired range of personalized / adaptive
learning features based on student profiles, pre-tests,
• Careful and close analysis of privacy concerns
Towards Predictive Learning Analytics
REINVENTING INSTRUCTIONAL DESIGN AND COURSE
Roots of Instructional Design – “Land” based
Transitions towards a New Model of Instructional Design
Course as product backward planning learning as
Singular “heroic efforts” integrated, cross disciplinary
Vertically integrated service delivery horizontally built
and managed teams
Faculty as consumer faculty as co-producer
Dominance of summative assessment models inclusion
of formative assessment models
Data production dataa New Model interpretation and
Towards consumption, of Instructional