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How to tell if your students are martIAns

An introduction to the what, where, who, and what-for of Analytics. How to tell if your students are martIAns. Contents (pg 1 of 7). What is “Analytics” Where is CCCOnline in terms of Learning Analytics? What is the Desire2Learn Analytics product? What can it actually do?

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How to tell if your students are martIAns

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  1. An introduction to the what, where, who, and what-for of Analytics How to tell if your students are martIAns

  2. Contents (pg 1 of 7) • What is “Analytics” • Where is CCCOnline in terms of Learning Analytics? • What is the Desire2Learn Analytics product? What can it actually do? • What have other institutions done? Where are other institutions going?

  3. What are “Learning Analytics” to us? • Analytics is processing data in some fashion that will help us do our jobs as administrators or instructors. • It is similar to and includes earlier fields/fads, such as “educational data mining”, but implies visualization of data so as to be made more useful to faculty and staff.

  4. What is CCCOnline up to • Desire2Learn progress tracking • Faculty in-attendance alerts • Student no-show reports • Desire2Learn Analytics • Behavior analysis

  5. D2L Progress Tool • Not graphical, all tables

  6. D2L Analytics – Faculty Portal • What are my students doing at a glance? • Tool use • Grade patterns

  7. Quiz Consistency Analysis “Does my quiz measure just one thing?”

  8. D2L Analytics Proper

  9. D2L Analytics – data domains • Sessions – “When have they been in their course?” • Tool use – “When did they go into the discussions?” • Content access – “What have they read?” • Difficulties with content • Grades • Various gradebook designs • Quiz question grades

  10. What are other institutions doing? • What is out there that we want to achieve as well? • Who is doing what? • Visualizing data • Standard reports - What happened? • Ad hoc reports - How many how often and were • Query/Drill down -Where exactly is the problem? • Alerts - What actions are needed? • Statistical Analytiss - Why is this happening? • Forecasting/Extrapoluation -What if these trends continue? • Predictive Modeling - What will happen next? • Optimization - What’s the best that can happen?

  11. KatholiekeUniversiteit Leuven“Monitor Widget” • Visually compare your time in class or resources accessed with your peers. • “Am I doing what I should be in order to be successful?”

  12. SNAPPUniversities of Queensland and Wollongong, AustraliaUniversity of British Columbia, Canada

  13. University of Belgrade“LOCO-Analyst”

  14. Local-AnalystContent Access & Analysis

  15. Loco-AnalystSocial Network Analysis

  16. Minnesota State College and Universities“Accountability dashboard”

  17. Predictive modeling

  18. Signals • http://www.itap.purdue.edu/tlt/signals/signals_final/index.htm

  19. Signals illustrated

  20. Signals Faculty Dashboard • Student success at a glance • Prepare and dispatch custom intervention E-mails

  21. American Public University System • For profit university serving over 80k online students. • Collects almost a hundred metrics based on student demographics, prior grades, and current course data. • Metrics are fed into a Neural Network that compares the metrics to grades in previous semesters, ranking the students from 1-80k in their chances of success. • The user can drill down to find out exactly what makes the network “think” a student will fail.

  22. Recommendation EngineFruanhoferInsituttion for Applied information Technology at FIT • Domain Ontology • + Usage patterns of prior users • + Identifying feature of “this” user – a search term, academic status, etc • = Recommended resources

  23. Another example of a recommendation engine…

  24. Semantic AnalysisOpen University, UK • Look into the content of posts to determine what style of communication it is. • Challenges eg But if, have to respond, my view • Critiques eg However, I’m not sure, maybe • Discussion of resources eg Have you read, more links • Evaluations eg Good example, good point • Explanations eg Means that, our goals • Explicit reasoning eg Next step, relates to, that’s why • Justifications eg I mean, we learned, we observed • Others’ perspectives eg Agree, here is another, take your point

  25. Ultimate Goal • Modeling/Predicting success • Staging the most effective interventions • Improving instructor abilities • Improving students’ self awareness • Customized learning • Learning Styles • Cognitive Load • The hierarchy of student success through Action Analytics • Raising Awareness (Analytics IQ) • Data, Information, and Analytics Tools and Applications • Embedded Analytics in student success processes • Culture of performance measurement and improvement • Optimized student success

  26. Dangers • “Analytics for learners rather than of learners” - Dragan Gasevic, Athabascau U. • Trapping students into limiting models of “good” behavior. • Disrupting and Transformative Innovation – Institutions resist change

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