how to tell if your students are martians
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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? - PowerPoint PPT PresentationTRANSCRIPT
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HOW TO TELL IF YOUR STUDENTS ARE MARTIANS
An introduction to the what, where, who, and what-for of Analytics
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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?
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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.
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What is CCCOnline up to Desire2Learn progress tracking
Faculty in-attendance alertsStudent no-show reports
Desire2Learn AnalyticsBehavior analysis
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D2L Progress Tool Not graphical, all tables
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D2L Analytics – Faculty Portal What are my students doing at a glance?
Tool useGrade patterns
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Quiz Consistency Analysis“Does my quiz measure just one thing?”
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D2L Analytics Proper
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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
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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?
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Katholieke Universiteit 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?”
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SNAPPUniversities of Queensland and Wollongong, AustraliaUniversity of British Columbia, Canada
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University of Belgrade“LOCO-Analyst”
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Local-AnalystContent Access & Analysis
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Loco-AnalystSocial Network Analysis
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Minnesota State College and Universities“Accountability dashboard”
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Predictive modeling
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Signals http://www.itap.purdue.edu/tlt/signals/sig
nals_final/index.htm
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Signals illustrated
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Signals Faculty Dashboard
Student success at a glance Prepare and dispatch custom
intervention E-mails
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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.
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Recommendation Engine Fruanhofer Insituttion 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
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Another example of a recommendation engine…
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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 viewCritiques eg However, I’m not sure, maybeDiscussion of resources eg Have you read, more linksEvaluations eg Good example, good pointExplanations eg Means that, our goalsExplicit reasoning eg Next step, relates to, that’s whyJustifications eg I mean, we learned, we observedOthers’ perspectives eg Agree, here is another, take
your point
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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
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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