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SAAIR 2014 keynote Sharon Slade
This document discusses learning analytics and some of the opportunities and issues regarding its use. It describes how learning analytics involves collecting and analyzing student data to better understand students and their learning needs. It provides examples of how some universities are using analytics to identify at-risk students and target interventions. However, it also notes some privacy and ethical concerns and the need for policies around transparent and responsible use of student data. The document advocates for student consent, engagement, and developing skills across institutions to ensure analytics are implemented ethically.
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SAAIR 2014 keynote Sharon Slade
- 1. Learning analytics ā opportunities and issues Dr Sharon Slade The Open University, UK 21st SAAIR Conference 2014 Yesterday, today and tomorrow: 21 years of Institutional Research
- 5. Learning analytics isthe measurement, collection, analysis and reporting of data about learners to increase our understanding of them and their learning needs, and to use that understanding to influence their learning.
- 6. ā99% of whoyou are is invisibleā R. Buckminster Fuller ā http://www.glennsasscer.com/wordpress/wp-content/ uploads/2011/10/iceberg.jpg
- 7. What do wemean by data about learners? Background Disability Gender Learning behaviours Posting to forums - content Ethnicity Study goals Study history Learning style Funding issues Assignment/test scores Websites visited Age Hitting study milestones Location Working status Family income Language Log in frequency Posting to forums - frequency Frequency of contact with tutor
- 8. Itās everywhere: everylearning tool now has an āanalytics dashboardā (a Google image search) 8
- 9. The OU analyticsinnovation to impact cycle annual cycles of quality enhancement and research capability and student number planning maintain analysis models manage data store responsibility for architecture Innovation Impact Research, benchmarking and rapid prototyping Data storage and access for analysis Data collection Technology architecture Actionable insight Learning design Information advice and guidance Informed student interventions Quality enhancement processes Define new research questions Innovation mainstreamed into operations Requirements for new data collection Manage core systems and support surveys ongoing insight build and manage systems and tools, provide process reports New indicators identified Mainstream indicators Mainstreamed analysis models Data harvesting from core student record and learning systems Systems Students Outcomes Evidence based learning design and continuous improvement ā increased student attainment over time More targeted and appropriate IAG ā decrease in early drop-out Interventions are better targeted and more effective ā increased retention over time Direct feedback from students Mainstream BAU http://www.jumpoffthescreen.com/analytics.php
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- 12. Purdueās Course signals ā¢ Uses a predictive model based on ā VLE activity and assessment scores ā¢ Previous academic history and demographic data ā¢ Has created an āearly warningā system which ā Identifies students āat riskā of not completing a course ā Deploys an intervention to increase chances of success ā¢ System automates the intervention process ā Student gets ātraffic lightā alert via VLE, and ā an email/message suggesting corrective action
- 13. Purdue University Signals:real time traffic-lights for students based on predictive model āResults thus far show that students who have engaged with Course Signals have higher average grades and seek out help resources at a higher rate than other students.ā Pistilli, M. D., Arnold, K. and Bethune, M., Signals: Using Academic Analytics to Promote Student Success. EDUCAUSE Review Online, July/Aug., (2012). http://www.educause.edu/ero/article/signals-using-academic-analytics-promote- student-success
- 14. Knewton (Arizona StateUniv) ā¢ A continuously adaptive online learning platform ā¢ Logs data about student behaviour and performance (e.g. keystrokes, scores, speed, etc) ā¢ Analyses behavioural and performance data, comparing it with similar students and assessing relevance of educational content to students ā¢ Serves each individual student the most appropriate learning activity for them at a particular moment in time
- 17. #Learning analytics asa digital Sorting Hat
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- 19. Celebrity photos scandala wake-up call for cloud users http://www.thebureauinvestigates.com/category/projects/surveillance-2/
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- 23. Developing new policy Drawing upon existing practice, existing literature No comparable policy within HE sector Sharon Slade and Paul Prinsloo, "Learning Analytics: Ethical Issues and Dilemmas," in American Behavioral Scientist, Vol. 57, 2013, p. 1514. doi: 10.1177/0002764213479366
- 24. New OU policyfor the ethical use of learning analytics Principle 1: Learning analytics is a moral practice, which should align with core organisational principles. Principle 2: The OU has a responsibility to all stakeholders to use and extract meaning from student data for the benefit of students where feasible.
- 25. Principle 3: Studentsare not wholly defined by their visible data or our interpretation of that data. Principle 4: The purpose and the boundaries regarding the use of learning analytics should be well defined and visible.
- 26. Principle 5: TheOU should aim to be transparent regarding data collection, and provide students with the opportunity to update their own data and consent agreements at regular intervals. Principle 6: Students should be engaged as active agents in the implementation of learning analytics (e.g. informed consent, personalised learning paths, interventions).
- 27. Principle 7: Modellingand interventions based on analysis of data should be sound and free from bias. Principle 8: Adoption of learning analytics within the OU requires broad acceptance of the values and benefits (organisational culture) and the development of appropriate skills across the organisation.
- 28. the strive forclarity https://www.flickr.com/photos/pentog/4495052859
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- 30. transparency of purpose https://www.flickr.com/photos/williamcromar/5338216221
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- 34. getting the balanceright http://www.educause.edu/ero/article/learning-analytics-and-ethics-framework-beyond-utilitarianism https://www.flickr.com/photos/pie4dan/4567311801