Transcript Fancy Intro - eLearning Consortium of Colorado

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.
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What is CCCOnline up to
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Desire2Learn progress tracking
 Faculty in-attendance alerts
 Student no-show reports
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Desire2Learn Analytics
 Behavior analysis
D2L Progress Tool
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Not graphical, all tables
D2L Analytics – Faculty Portal
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What are my students doing at a glance?
 Tool use
 Grade 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?”
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 Difficulties with content
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Grades
 Various gradebook designs
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Quiz question grades
What are other institutions
doing?
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What is out there that we want to achieve as
well?
Who is doing what?
Visualizing data
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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”
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Visually compare your time in class or resources
accessed with your peers.
“Am I doing what I should be in order to be
successful?”
SNAPP
Universities of Queensland and Wollongong, Australia
University of British Columbia, Canada
University of Belgrade
“LOCO-Analyst”
Local-Analyst
Content Access & Analysis
Loco-Analyst
Social Network Analysis
Minnesota State College and Universities
“Accountability dashboard”
Predictive modeling
Signals

http://www.itap.purdue.edu/tlt/signals/sig
nals_final/index.htm
Signals illustrated
Signals Faculty Dashboard
Student success at a glance
 Prepare and dispatch custom
intervention E-mails
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American Public University
System
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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 Analysis
Open 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
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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
Ultimate Goal
Modeling/Predicting success
Staging the most effective interventions
 Improving instructor abilities
 Improving students’ self awareness
 Customized learning
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 Learning Styles
 Cognitive Load
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The hierarchy of student success through Action
Analytics
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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
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