1. Learning analytics opportunities and issuesDr Sharon SladeThe Open University, UK21st SAAIR Conference 2014Yesterday, today and tomorrow: 21 years of Institutional Research

2. Learning analytics is the measurement,collection, analysis and reporting of data aboutlearners to increase our understanding of themand their learning needs, and to use thatunderstanding to influence their learning. 3. 99% of who you are is invisibleR. Buckminster Fullerhttp://www.glennsasscer.com/wordpress/wp-content/uploads/2011/10/iceberg.jpg 4. What do we mean by data aboutlearners?BackgroundDisabilityGenderLearningbehavioursPosting to forums -contentEthnicityStudy goalsStudy historyLearning styleFundingissuesAssignment/testscoresWebsites visitedAgeHitting studymilestonesLocationWorking statusFamily incomeLanguageLog in frequencyPosting to forums -frequencyFrequency of contactwith tutor 5. Its everywhere: every learning tool now has an analyticsdashboard (a Google image search)8 6. The OU analytics innovation to impact cycleannual cycles of quality enhancement andresearch capabilityand studentnumber planningmaintain analysismodelsmanage data storeresponsibility forarchitectureInnovation ImpactResearch,benchmarking andrapid prototypingData storage and access for analysisData collectionTechnology architectureActionable insightLearning designInformation adviceand guidanceInformed studentinterventionsQualityenhancementprocessesDefine newresearch questionsInnovationmainstreamedinto operationsRequirements fornew datacollectionManage coresystemsand supportsurveysongoing insightbuild and manage systems and tools, provide processreportsNew indicatorsidentifiedMainstreamindicatorsMainstreamedanalysis modelsData harvesting from core studentrecord and learning systemsSystems StudentsOutcomesEvidence basedlearning design andcontinuousimprovement increased studentattainment overtimeMore targeted andappropriate IAG decrease in earlydrop-outInterventions arebetter targeted andmore effective increasedretention over timeDirect feedback from studentsMainstream BAUhttp://www.jumpoffthescreen.com/analytics.php 7. Recommender 8. Purdues Course signals Uses a predictive model based on VLE activity and assessment scores Previous academic history and demographic data Has created an early warning system which Identifies students at risk of not completing a course Deploys an intervention to increase chances of success System automates the intervention process Student gets traffic light alert via VLE, and an email/message suggesting corrective action 9. Purdue University Signals: real time traffic-lights forstudents based on predictive modelResults thus far show that studentswho have engaged with Course Signalshave higher average grades and seekout help resources at a higher ratethan other students.Pistilli, M. D., Arnold, K. and Bethune, M., Signals: Using Academic Analytics toPromote Student Success. EDUCAUSE Review Online, July/Aug., (2012).http://www.educause.edu/ero/article/signals-using-academic-analytics-promote-student-success 10. Knewton (Arizona State Univ) A continuously adaptive online learningplatform Logs data about student behaviour andperformance (e.g. keystrokes, scores, speed,etc) Analyses behavioural andperformance data, comparing itwith similar students andassessing relevance ofeducational content to students Serves each individual studentthe most appropriate learningactivity for them at a particularmoment in time 11. #Learning analytics as a digital Sorting Hat 12. https://www.flickr.com/photos/uncloned/5370399502 13. Celebrity photos scandal a wake-up callfor cloud usershttp://www.thebureauinvestigates.com/category/projects/surveillance-2/ 14. https://www.youtube.com/watch?v=F7pYHN9iC9I 15. https://www.flickr.com/photos/zigazou76/5824384001/sizes/z 16. https://www.flickr.com/photos/jes8jes/9655367348 17. Developing new policyDrawing upon existing practice, existing literatureNo comparable policywithin HE sectorSharon Slade and Paul Prinsloo, "Learning Analytics: Ethical Issues and Dilemmas," inAmerican Behavioral Scientist, Vol. 57, 2013, p. 1514. doi: 10.1177/0002764213479366 18. New OU policy for the ethical useof learning analyticsPrinciple 1: Learning analytics is a moralpractice, which should align with coreorganisational principles.Principle 2: The OU has a responsibility to allstakeholders to use and extract meaning fromstudent data for the benefit of students wherefeasible. 19. Principle 3: Students are not wholly defined bytheir visible data or our interpretation of thatdata.Principle 4: The purpose and the boundariesregarding the use of learning analytics should bewell defined and visible. 20. Principle 5: The OU should aim to be transparentregarding data collection, and provide studentswith the opportunity to update their own dataand consent agreements at regular intervals.Principle 6: Students should be engaged as activeagents in the implementation of learning analytics(e.g. informed consent, personalised learningpaths, interventions). 21. Principle 7: Modelling and interventions basedon analysis of data should be sound and freefrom bias.Principle 8: Adoption of learning analyticswithin the OU requires broad acceptance of thevalues and benefits (organisational culture) andthe development of appropriate skills across theorganisation. 22. the strive for clarityhttps://www.flickr.com/photos/pentog/4495052859 23. https://www.flickr.com/photos/savvyduck/7903341834 24. transparency of purposehttps://www.flickr.com/photos/williamcromar/5338216221 25. https://www.flickr.com/photos/rooreynolds/46541511 26. https://www.flickr.com/photos/nffcnnr/5399478788 27. getting the balance righthttp://www.educause.edu/ero/article/learning-analytics-and-ethics-framework-beyond-utilitarianismhttps://www.flickr.com/photos/pie4dan/4567311801