Keynote address The power of learning analytics: a need to move towards new methodologies in education? @DrBartRienties VU Amsterdam, 13 October 2016 Reader in Learning Analytics A special thanks to Avinash Boroowa, Aida Azadegan, Shi-Min Chua, Simon Cross, Rebecca Ferguson, Lee Farrington-Flint, Christothea Herodotou, Martin Hlosta, Wayne Holmes, Garron Hillaire, Simon Knight, Nai Li, Vicky Marsh, Kevin Mayles, Jenna Mittelmeier, Vicky Murphy, Quan Nguygen, Tom Olney, Lynda Prescott, John Richardson, Jekaterina Rogaten, Matt Schencks, Mike Sharples, Dirk Tempelaar, Lisette Toetenel, Thomas Ullmann, Denise Whitelock, John Woodthorpe, Zdenek Zdrahal, and others…A special thanks to Prof Belinda Tynan for her continuous support on analytics at the OU UK
What is learning analytics? http://bcomposes.wordpress.com/
(Social) Learning Analytics “LA 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” (LAK 2011) Social LA “focuses on how learners build knowledge together in their cultural and social settings” (Ferguson & Buckingham Shum, 2012)
Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and Implementation of a Learning Analytics Toolkit for Teachers. Journal of Educational Technology & Society, 15 (3), 58-76.
Agenda? You choose 1. The power of 151 Learning Designs on 113K+ students at the OU? 2. Analytics4Action: evidence-based interventions? 3. OU Analyse: predictive analytics with automated student recommender? 4. Key drivers for 100K+ student satisfaction? 5. Opportunities of learning analytics/elearning for educational research, teaching practice, and wider policy implications.
So what does the OU do in terms of interventions on learning analytics?
Strategic approach The OU is developing its capabilities in 10 key areas that build the underpinning strengths required for the effective deployment of analytics
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Analytics4Action framework Key metrics and Menu of response Methods of Evaluation Plans drill downs actions gathering data Community Implementation/testing methodologies Deep dive of inquiry analysis and framework: • Randomised control trials • Quasi-experimental strategic insight underpinning • A/B testing • Apply to all typology Evidence hub 11
Menu of actions Learning design (before start) In-action interventions (during module) • • Cognitive Presence Redesign learning materials Audio feedback on assignments • • Redesign assignments Bootcamp before exam • • Social Presence Introduce graded discussion forum activities Organise additional videoconference sessions • • Group-based wiki assignment One-to-one conversations • • Assign groups based upon learning analytics Cafe forum contributions • metrics Support emails when making progress • Emotional questionnaire to gauge students’ emotions • Introduce buddy system • • Teaching Presence Introduce bi-weekly online videoconference Organise additional videoconference sessions • sessions Call/text/skype student-at-risk • • Podcasts of key learning elements in the module Organise catch-up sessions on specific topics that • Screencasts of “how to survive the first two weeks” students struggle with 14
Selected important VLE activities • Forum (F), Subpage (S), Resource (R), OU_content (O), No activity (N) • Possible activity combinations in a week: F, FS, N, O, OF, OFS, OR, ORF, ORFS, ORS, OS, R, RF, RFS, RS, S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S
Activity space Start VLE opens F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S Start F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S Pass Fail No submit TMA-1 time
VLE trail: successful Start student VLE opens F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S Start F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S Pass Fail No submit TMA-1 time
VLE trail: student who did Start not submit VLE opens F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S Start F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S F FS N O OF OFS OR ORF ORFS ORS OS R RF RFS RS S Pass Fail No submit TMA-1 time
Probabilistic model: all students time VLE start TMA1
Module VLE Fingerprint
Dashboard: Module view Prediction table
Dashboard: Student view VLE activities, TMA results, time machine
Dashboard: Student view Nearest neighbours, Predictions with real scores, Personalised recommender
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