Learning Analytics is an emerging topic of interest throughout all levels of education focusing on how to harness the power of data mining, interpretation, and modeling.
However, there are several similar terms (academic analytics, predictive analytics, business intelligence, etc.) that can confuse educators and administrators alike. In this session, we will unpack this new area of interest and discuss how institutions can begin to leverage available products and open source communities to utilize analytics to improve understandings of teaching and learning and to tailor education more effectively.
We will briefly present an overview of the learning analytics field, drawing from popular examples such as the Signals project at Purdue U. and the Check My Activity tool at U. Maryland, Baltimore County. We will also review the structure of Sakai CLE and OAE user-level metrics and briefly discuss projects to design and implement tools to utilize these metrics in meaningful ways.
1. Steve Lonn, University of Michigan
Josh Baron, Marist College
June 10-15, 2012
Growing Community;
Growing Possibilities
2. 1. What is Learning Analytics (LA)?
2. Current LA work in Higher Education
3. Data available in Sakai CLE & OAE
4. Big Questions to Ponder
5. Q&A
Slides Available: slideshare.net/stevelonn/
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3. “...datasets whose size is beyond
the ability of typical database
software tools to capture, store,
manage, and analyze.”
Manyika et al. (2011)
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5. Analytics:
An overarching concept that
is defined as data-driven
decision making
van Barneveld, Arnold, & Campbell, 2012
adapted from Ravishanker
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7. Business / Academic Analytics:
A process for providing higher
education institutions with the
data necessary to support
operational and financial
decision making
van Barneveld, Arnold, & Campbell, 2012
adapted from Goldstein and Katz
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8. evidenceframework.org/big-data/
Educational Data Mining
Learning Analytics
Bienkowski, Feng, &
Means, 2012
◦ SRI International
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9. Generally emphasizes reduction into small, easily
analyzable components
◦ Can be then adapted to student by software
◦ Siemens and Baker, 2012
Predicting future learning behavior
Domain models for content / sequences
Software-provided pedagogical supports
Computational models that incorporate student,
domain, and pedagogy
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11. Educational Data Mining:
A process for analyzing data
collected during teaching and
learning to test learning
theories and inform
educational practice
Bienkowski, Feng, & Means, 2012
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12. Understand entire systems and support
human decision making
Applies known methods & models
◦ answer questions about learning and organizational
learning systems
Tailored responses
◦ adapted instructional content, specific
interventions, providing specific feedback
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13. Learning Analytics:
The use of analytic techniques to
help target instructional, curricular,
and support resources to support the
achievement of specific learning
goals through applications that
directly influence educational practice
van Barneveld, Arnold, & Campbell, 2012
adapted from Bach
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14. Predictive Analytics
◦ uncover relationships and patterns
◦ can be used to predict behavior and events
Visual Data Analytics
◦ discovering and understanding patterns in large
datasets via visual interpretation
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15. Term Definition Level of
Focus
An overarching concept that is defined as data-
Analytics All levels
driven decision making
A process for providing higher education
Academic
institutions with the data necessary to support Institution
Analytics
operational and financial decision making
A process for analyzing data collected during Department /
Educational
teaching and learning to test learning theories Instructor /
Data Mining Learner
and inform educational practice
The use of analytic techniques to help target
instructional, curricular, and support resources Department /
Learning Instructor /
to support the achievement of specific learning
Analytics Learner
goals through applications that directly influence
educational practice
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16. Who‟s been working in this
space in Higher Education?
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18. Built predictive model using data from…
◦ LMS – Events (login, content, discuss.) & gradebook
◦ SIS – Aptitude (SAT/ACT, GPA) & demographic data
Leverage model to create Early-alert system
◦ Identify students at risk to not complete the course
◦ Deploy intervention to increase chances of success
Systems automates intervention process
◦ Students get “traffic light” alert in LMS
◦ Messages are posted to student that
suggest corrective action (practice tests)
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19. Impact on course grades and retention
◦ Students in courses using Course Signals…
scored up to 26% more A or B grades
up to 12% fewer C's; up to 17% fewer D's and F„s
Ellucian product that integrates w/Blackboard
Open Academic Analytics Initiative (OAAI)
◦ Creating a similar Sakai-based OS solution
Arnold & Pistilli, 2012 - LAK
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20. Focused specifically on introductory Physics
Uses data from…
◦ Pre-course survey: academic info,
learner‟s goals, psycho-social factors
◦ Performance: Exams, Web HW, Sakai
Michigan Tailoring System (MTS)
◦ OS tool designed for highly customized messaging
◦ Used in health sciences for behavior change
◦ Messaging based on input from many sources
“…to say to each what we would say if we
could sit down with them for a personal chat.”
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22. UMBC found that students
earning D/F‟s use Bb 39% less
then higher grade achievers
◦ Not suggesting cause and effect
◦ Goal is to model higher achiever
behavior
Provides data directly student
◦ Compare LMS use to class averages
◦ Can also compare averages usage
data to grade outcomes
Feedback has been positive
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23. Student Success Plan – Sinclair CC
◦ Holistic case-management system
◦ Connects faculty, advisors, counselors, & students
◦ Jasig Incubation Project
STAR Academic Journey – U of Hawaii
◦ Online advising and degree attainment system
SNAPP – UBC/Wollongong
◦ Visualize networks of
interaction resulting from
discussion forum posts and
replies
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24. Papers and Articles on Purdue‟s Course Signals
http://www.itap.purdue.edu/learning/research/
Michigan‟s Expert Electronic Coaching
http://sitemaker.umich.edu/ecoach/home
UMBC‟s Check My Activity Tool
http://www.educause.edu/EDUCAUSE+Quarterly/EDUCAUSEQuarterlyMagazineVolum/Vi
deoDemoofUMBCsCheckMyActivit/219113
Student Success Plan
http://www.educause.edu/EDUCAUSE+Quarterly/EDUCAUSEQuarterlyMagazineVol
um/TheStudentSuccessPlanCaseManag/242785
STAR Academy Journey
http://net.educause.edu/ir/library/pdf/pub7203cs7.pdf
SNAPP
http://research.uow.edu.au/learningnetworks/seeing/snapp
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25. What can we know in CLE and
OAE products?
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26. User-level data stored as “events”
sakai_event sakai_session
EVENT_ID SESSION_ID
EVENT_DATE SESSION_USER
EVENT SESSION_IP
REF SESSION_USER_AGENT
SESSION_ID SESSION_START
EVENT_CODE SESSION_END
CONTEXT SESSION_SERVER
SESSION_ACTIVE
SESSION_HOSTNAME
List of events available on Confluence
◦ Search for “event table description”
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27. Site-level data stored in separate tables
sakai_site sakai_realm
CUSTOM_PAGE_ORDERED REALM_KEY
SITE_ID REALM_ID
TITLE PROVIDER_ID
TYPE MAINTAIN_ROLE
SHORT_DESC CREATEDBY
DESCRIPTION MODIFIEDBY
ICON_URL CREATEDON
INFO_URL MODIFIEDON
SKIN
PUBLISHED
JOINABLE
PUBVIEW
JOIN_ROLE
CREATEDBY
MODIFIEDBY
CREATEDON realm_id like '/site/' || site_id
MODIFIEDON
IS_SPECIAL
IS_USER
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29. 3% 2% 1% 1%1%
Presence
3% Web Content
Resources
Attachments
43% Test Center
18%
Assignments
Syllabus
Forums
Gradebook
Drop Box
25%
Evaluations
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30. Social Work
Architecture
Engineering
Business
LS&A
Education
Public Health
Art & Design
Law
Nursing
Music
Medicine
Dentistry
Pharmacy
0% 25% 50% 75% 100%
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31. Clinical Assoc Prof
Clinical Lecturer
Clinical Professor
Clinical Asst Prof
Asst Professor
Professor
Assoc Professor
Asst Professor
Adjunct Clin Asst Professor
Adjunct Clinical Lecturer
Adjunct Clin Assoc Prof
0% 25% 50% 75% 100%
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32. BIT
Sum of Revisions ENGR
NURS
IOE
SI
ENGLISH
RCHUMS
AAAS
EECS
RCLANG
Count of Course Sites
SI
ENGLISH
BIT
EECS
PSYCH
COMP
MODGREEK
NURS
NRE
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33. Summary information about site visits, tool
activity, and resource activity
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35. User-level data available via “activity feeds”
◦ follows a “push and publication” model rather than
a “store and query” model (CLE is store & query)
◦ Activity is both highly specific: individual
interactions between users, content, contexts…
◦ …and more general: user interaction everywhere
rather than only within a single course context.
What new questions will we ask?
◦ Interesting activity can happen with external
capabilities: CLE tools, LTI tools, widgets.
How will we ensure this data is captured?
Many thanks to Nate Angell for OAE slides
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36. FYI: Designs are still in draft form.
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37. FYI: Designs are still in draft form.
37
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38. FYI: Designs are still in draft form.
38
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39. Activity (OAE) & Grades (CLE): Week 1
Developed by the Kaleidoscope Project in collaboration with rSmart.
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40. Activity (OAE) & Grades (CLE): Week 7
Developed by the Kaleidoscope Project in collaboration with rSmart.
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41. Activity (OAE) & Grades (CLE): Animation
Developed by the Kaleidoscope Project in collaboration with rSmart.
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42. Tools / services to support analytics
initiatives
◦ Ways to connect different silos of data
◦ Methods to connect back to CLE / OAE
LTI? Web services? Others?
OAE improvements over CLE approach to user
data
◦ What data is most relevant for analytics?
◦ What displays and/or data are most useful to help
learners?
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44. Data Mining vs. Learning Science Approaches
◦ Do we build predictive models from large data sets
or from our understanding of learning sciences?
◦ Is both the right answer? How does that work?
Challenges of Scaling LA Across Higher Ed
◦ Does each institution have to build its own model?
How “portable” are predictive models?
◦ Do we need an open standard for LA? Could LIS and
LTI play a role?
How can LA be used to assist ALL students?
◦ Michigan‟s E2Coach system is a good example
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45. “The obligation of knowing” – John Campbell
◦ If we have the data and tools to improve student
success, are we obligated to use them?
Consider This > If a student has a 13% chance of
passing a course, should they be dropped? 3%?
Who owns the data, the student? Institution?
◦ Should students be allowed to “opt out”?
Consider This > Is it fair to the other students if by
opting out the predictive model‟s power drops?
What do we reveal to students? Instructors?
Consider This > If we tell a student in week three they
have a 9% chance of passing, what will they do?
Will instructors begin to “profile” students?
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47. http://www.solaresearch.org/
Learning Analytics & Knowledge Conferences (LAK)
STORM – initiative to help fund research projects
FLARE – regional practitioner conference
◦ Purdue University, Oct 1-3, 2012
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48. Symposium on Learning Analytics at Michigan
http://sitemaker.umich.edu/slam/
15 speakers (12 UM, 3 external)
Videos & slides available from all speakers
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49. Analytics in Higher Education: Establishing a Common Language
◦ Van Barneveld, Arnold, Campbell, 2012
◦ http://www.educause.edu/Resources/AnalyticsinHigherEducationEsta/245405
Analytics to Literacies: Emergent Learning Analytics to evaluate new literacies
◦ Dawson, 2011- http://blogs.ubc.ca/newliteracies/files/2011/12/Dawson.pdf
Learning Analytics: Definitions, Process Potential
◦ Elias, 2011
◦ http://learninganalytics.net/LearningAnalyticsDefinitionsProcessesPotential.pdf
The State of Learning Analytics in 2012: A Review and Future Challenges
◦ Ferguson, 2012 - http://kmi.open.ac.uk/publications/pdf/kmi-12-01.pdf
Academic analytics: A new tool for a new era.
◦ Campbell, Deblois, & Oblinger (2007). Educause Review, 42(4), 40-57.
◦ http://net.educause.edu/ir/library/pdf/ERM0742.pdf
Mining LMS data to develop an "early warning system" for educators: A proof of concept.
◦ Macfadyen & Dawson (2010) - Computers & Education, 54(2), 588-599.
Classroom walls that talk: Using online course activity data of successful students to raise
self- awareness of underperforming peers.
◦ Fritz, 2011 - Internet and Higher Education, 14(2), 89-97.
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50. Wednesday, 13 June
◦ Learning Analytics: A Panel Debate on the
Merits, Methodologies, and Related Issues (1:15pm)
◦ Learning Analytics at Michigan: Designing Displays for
Advisors, Instructors, and Students (2:30pm)
◦ BOF for Learning Analytics: Current and Planned Projects
and Tools (3:45pm)
Thursday, 14 June
◦ Creating an Open Ecosystem for Learner Analytics
(10:15am)
Open Academic Analytics Initiative (OAAI)
https://confluence.sakaiproject.org/x/8aWCB
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Headlines from THIS MONTH that mention Big Data\\But Big Data is not just Google, Facebook, Twitter, Amazon, and Netflix…
Student Information SystemsLearning Management SystemsBroad-Based SurveysPublisher journals & eTextbooksVideo contentLecture captureEportfoliosAnd more…
Student Information SystemsLearning Management SystemsBroad-Based SurveysPublisher journals & eTextbooksVideo contentLecture captureEportfoliosAnd more…
HousingAdmissionsFinancial AidHuman ResourcesDepartments & Units’ own metrics
HousingAdmissionsFinancial AidHuman ResourcesDepartments & Units’ own metrics
Generally, educational data mining is looking for new patterns in data and developing new algorithms and/or new models, while learning analytics is applying known predictive models in instructional systems.
Common Questions for EDM:What sequence of topics is most effective for a specific student? What student actions are associated with more learning (e.g., higher course grades)? What student actions indicate satisfaction, engagement, learning progress, etc.? What features of an online learning environment lead to better learning? What will predict student success?
Math tutor – gives hints where stuck, data about progress can inform next question type & difficulty
When are students ready to move onto the next topic? When are students falling behind in a course? When is a student at risk for not completing a course? What grade is a student likely to get without intervention? What is the best next course for a given student? Should a student be referred to a counselor for help?
When are students ready to move onto the next topic? When are students falling behind in a course? When is a student at risk for not completing a course? What grade is a student likely to get without intervention? What is the best next course for a given student? Should a student be referred to a counselor for help?
Josh’s Section
Steve’s Section
REF changes depending on event typeAt Michigan and other institutions of similar size, both of these tables are archived and saved into non-production copies, so only the last 24-36 hours of data is typically located on the production servers. This also allows analytics work to occur without impacting production.
Most tools also have their own tables with relevant data such as Resources, Gradebook, Forums, etc. Due to the open-source nature of these tools, the structure of the data and how to query relevant information is often difficult and not uniform across tools
Here is a graph of different site types and maximum number of concurrent users per term that is calculated from data in the sakai_site tableCan be useful for institutional decisions about servers, space, etc.
Query using information from the HR database matched against the sakai_session tableWinter 2010 data
Winter 2010 data
Generate reports based on: site visits tool events resource activityNarrow search by:specific tools, events or resourcespre-defined or custom time periodsall/specific users, roles, groups or non-matching usersExcel, CSV and PDF file export
Generate reports based on: site visits tool events resource activityNarrow search by:specific tools, events or resourcespre-defined or custom time periodsall/specific users, roles, groups or non-matching usersExcel, CSV and PDF file export
Activity feeds will be the next feature that is added to OAE, and is currently already in development. They will show user activity from different perspectives (e.g. all activity relevant to me, all activity related to a course, etc.) and include things like someone uploading a file, someone commenting on a file, a person you follow updating his profile, someone participating in a discussion, etc.Whilst CLE has often collected quite technical user-level information in the sakai-event table, OAE is taking an approach where it tries to separate technical events/activity from user level events/activity. Some of these user-level events will be captured and stored automatically, and perhaps shipped off to a data warehouse for analytics. But an important part of OAE analytics will also be the ability for end-users to determine what they want to capture for a given context (e.g. How long was the student here", "How many minutes of the video did he watch" and "After how many questions did this student stop filling out this quiz"). This would be done using a JavaScript API and a UI wrapper for that API, and that data can then also be fed into the analytics process.
If we determine a student is “at risk”, are we obligated to intervene?
Also MOOCs, new Journal, etc.
Great way to start conversation at your own institution and connect faculty & researchers who are working with student data, but may not think they are doing learning analytics
Also session this morning about work at University of HullAnd sessions about Jasig projects like Student Success Plan (Tuesday morning)