HOW TO TELL IF YOUR STUDENTS ARE MARTIANS

An introduction to the what, where, who, and what-for of Analytics

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?

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.

What is CCCOnline up to

Desire2Learn progress trackingFaculty in-attendance alertsStudent no-show reports

Desire2Learn AnalyticsBehavior analysis

D2L Progress Tool Not graphical, all tables

D2L Analytics – Faculty Portal What are my students doing at a glance?

Tool useGrade patterns

Quiz Consistency Analysis

“Does my quiz measure just one thing?”

D2L Analytics Proper

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

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?

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?”

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

University of Belgrade“LOCO-Analyst”

Local-AnalystContent Access & Analysis

Loco-AnalystSocial Network Analysis

Minnesota State College and Universities“Accountability dashboard”

Predictive modeling

Signals

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

Signals illustrated

Signals Faculty Dashboard

Student success at a glance Prepare and dispatch custom

intervention E-mails

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.

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

Another example of a recommendation engine…

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

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

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