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June 21 learning analytics overview
1) Learning analytics involves measuring, collecting, analyzing, and reporting data about learners in order to better understand their learning needs and influence their learning. Universities are using student data to trigger interventions aimed at supporting students and improving outcomes. 2) Purdue and Arizona State University have developed early warning systems that use predictive models based on student data to identify at-risk students and deploy automated or staff-delivered interventions. The University of Maryland provides a tool allowing students to compare their online engagement to peers. 3) The Open University is piloting student support teams organized by curriculum rather than geography, using a monitoring tool powered by learning analytics to identify and track students against milestones and link them to appropriate interventions. Initial results
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June 21 learning analytics overview
- 1. Learning Analytics andHigher Education: a brief introduction Sharon Slade
- 2. Overview of session •Brief background • How learning analytics is being used in Higher Education • What the OU is doing • Things to think about
- 3. Our students leaveinformation about themselves every time they interact with us With no realization or understanding of what we do with that information So, how do we use that data – and does it matter?
- 4. 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.
- 5. What do wemean by data about learners? Background Disability Gender Ethnicity Learning behaviours Study history Learning style Funding issues Assignment/test scores Websites visited Hitting study milestones Study goals Age Location Working status Family income Language Log in frequency Posting to forums - frequency Frequency of contact with tutor Posting to forums - content
- 6. What’s going onin learning analytics? • Many universities are using student data to trigger interventions –Some are automated and direct to students –Others are delivered via the tutor or support staff –Most focus on online engagement, assessment and demographics • Broader studies are looking at modifying student learning as well as providing student support • Lots of newer work around social networking and how students engage with each other (and how that impacts on their success)
- 7. Purdue’s Course signals •Uses a predictive model based on – online 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 their online student page, and – an email/message suggesting corrective action
- 8. Knewton (Arizona StateUniv) • A continuously adaptive online learning platform • Logs data about student behaviour and performance (e.g. keystrokes, scores, speed, etc) • Analyses behavioural and performance data, comparing it with similar students and assessing relevance of educational content to students • Serves each individual student the most appropriate learning activity for them at a particular moment in time
- 9. University of Maryland’s ‘Checkmy activity’ Tool • Allows students to compare their use of the VLE against that of other students • Results indicate that students with lower usage score less well • http://www.screencast.com/t/jmZzozpPRZiG
- 10. The OU andStudent Support Teams • From Feb 2014, supporting students by curriculum rather than geography • Ensuring that students get proactive support based on their characteristics and study behaviours • Underpinned by a standard ‘service level agreement’ to ensure equitable treatment and the maintenance of high standards of support • The opportunity for development of expertise
- 11. The OU StudentSupport Tool • A monitoring tool to be used by Student Support Teams • Pulls in data relevant to the student • Aims to identify and track student progression against key milestones across a curriculum area • Links to interventions direct to students or via other staff
- 12. Select the SST, quals,pathways, modules, levels, regions of interest
- 13.
- 14. Run a reportto identify students who meet certain characteristics Students aged 50+
- 15. Review list of students Can sortand search each column
- 16. Send an intervention if needed Canopt to exclude certain student types
- 17. Student Support TeamsPilots • This approach has been piloted across faculties for the last few years • Has led to greater understanding of drivers of student success • Support staff feel more knowledgeable • Improvements in student retention and progression, as well as increased student satisfaction
- 18. So learning analyticscan help us to really understand our students. Sounds great, yes? Most research has focused on data protection and privacy issues, but is there more to it than that? What other issues might we be concerned about?
- 19. Privacy Do students appreciatethat information is being gathered about them? Are we explicit about what we might do with that information?
- 20. Transparency and robustness Whocan see the data collected? Who can see/influence the models? How reliable and robust are the models?
- 21. Power Who gets todecide what happens next? Who can choose which students get more support? Do teachers, learners, and administrators have the same authority/rights to determine what support is provided? ------ less
- 22. Ownership issues Who canmine our data for other purposes? Can students opt out of having their information used? … and what are the consequences of that? How long is data kept for?
- 23. Responsibilities Is there ashared responsibility to ensure that information is accurate? Can students opt to disguise themselves online? Do we have a responsibility to ensure equitable treatment of students based on what we know? (or despite what we know)
- 24.