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Innovation: Big Data and Analytics George Siemens, PhD NEASC December 9, 2015.

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Innovation: Big Data and Analytics George Siemens, PhD NEASC December 9, 2015
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Page 1: Innovation: Big Data and Analytics George Siemens, PhD NEASC December 9, 2015.

Innovation: Big Data and Analytics

George Siemens, PhDNEASC

December 9, 2015

Page 2: Innovation: Big Data and Analytics George Siemens, PhD NEASC December 9, 2015.

This system is being unbundled & rebundled,

creating new power and influence structures

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We don’t have the data or the models for understanding how dramatic changes now occurring

will impact higher education

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Lack of data-informed decision making

culture

Macfadyen, L., & Dawson, S. (2012). Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan. Educational Technology & Society, 15(3), 149-163.

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Siemens, Long, 2011. EDUCUASE Review

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The WHY of learning analytics

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“If the ladder of educational opportunity rises high at the doors of some youth and scarcely rises at the doors of others, while at the same time formal education is made a prerequisite to occupational and social advance, then education may become the means, not of eliminating race and class distinctions, but of deepening and solidifying them.”

President Truman, 1947

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Pell Institute, 2015

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McKinsey Quarterly, 2012

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Student profiles

Diversifying(OECD)

Less than 50% now full time(US Census Bureau)

http://www.oecd.org/edu/skills-beyond-school/EDIF%202013--N%C2%B015.pdf http://www.census.gov/prod/2013pubs/acsbr11-14.pdf

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Favours women over menMore learners as % (up to 60%)Average entrance age increasingTop three countries for entering students:

China, India, USATraditional science courses waning in popularityGreater international student

OECD 2013

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Enrolment: “perfect storm of challenges ahead”

University Business, January 2015

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To understand what tomorrow’s education system will look like, we have to understand the architecture of information today:

how is it createdhow is it sharedhow is it iterated

how is it controlled?

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Parallel developing partners: Adaptive and personalized

learningPlatform Publisher

Knewton PearsonSmart Sparrow McGraw-HillDesire2Learn adaptcoursewareLoudCloud CMU OLI

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Knowledge development, learning, is (should be) concerned with learners understanding relationships, not simply memorizing facts.

i.e. naming nodes is “low level” knowledge activity, understanding node connectivity, and implications of changes in network structure, consists of deeper, coherent, learning

Page 26: Innovation: Big Data and Analytics George Siemens, PhD NEASC December 9, 2015.

Granularization of assessment

Cracking the credit hour (New America Foundation)

Badges(Mozilla & others)

http://newamerica.net/publications/policy/cracking_the_credit_hour http://openbadges.org/

Page 27: Innovation: Big Data and Analytics George Siemens, PhD NEASC December 9, 2015.

Educational Quality through Innovative Partnerships (EQUIP)

Page 28: Innovation: Big Data and Analytics George Siemens, PhD NEASC December 9, 2015.

Certificates

Fastest growing form of credentialing (800% increase in 30 years)

Industry-facing

Carnevale, Rose, Hanson 2012

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Competencies

Competency-based degrees(Chronicle, 2014)

Prior learning assessment(Insider Higher Ed, 2012)

http://chronicle.com/article/Competency-Based-Degrees-/144769/ http://www.insidehighered.com/news/2012/05/07/prior-learning-assessment-catches-quietly

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Knowledge in pieces

diSessa, 1993

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“The world is one big data problem”Gilad Elbaz

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The WHAT of learning analytics

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In: Siemens, Gasevic, & Dawson (eds), 2015

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Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs.

LAK11 Conference

Page 37: Innovation: Big Data and Analytics George Siemens, PhD NEASC December 9, 2015.

Learning analytics is about learning

Gašević, D., Dawson, S., Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.

Page 38: Innovation: Big Data and Analytics George Siemens, PhD NEASC December 9, 2015.

Once size fits all does not work in learning analytics

Gašević, D., Dawson, S., Rogers, T., Gašević, D. (2016). Learning analytics should not promote one size fits all: The effects of course-specific technology use in predicting academic success. The Internet and Higher Education, 26, 68–84.

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“a team at Google couldn’t decide between two blues, so they’re testing 41 shades between each blue to see which one performs better”

Douglas Bowman

Page 40: Innovation: Big Data and Analytics George Siemens, PhD NEASC December 9, 2015.

What will LA do for learning science & education

Add a new research layerPersonalizationOptimization (move from negative orientation)Organizational insightImproved decision makingNew models of learningIncrease competitivenessImprove marketing/promotion/recruitment

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Blending physical and digital spaces

WearablesAmbient computingIoT

…biometric/physiological data needed to answer complex questions around social and affective being and learning

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The HOW of learning analytics

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This system is being unbundled & rebundled,

creating new power and influence structures


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