This document provides a summary of a presentation on learner analytics. It begins with an outline of the presentation which includes situating analytics, academic analytics using a case study of CSU's data dashboard, learner analytics using a case study of CSU Chico, promising efforts and resources, and a question and answer section. It then discusses academic analytics and how it uses large data sets and statistical modeling to improve decision making. A case study is presented on CSU's graduation initiative data dashboard which tracks metrics to increase graduation rates. Finally, a case study is presented on learner analytics research conducted at CSU Chico analyzing relationships between LMS tool usage and student achievement.

1. Learner Analytics
Beyond the Buzz
DETCHE Conference 2011
Kathy Fernandes
Download presentation at:
Scott Kodai
http://slidesha.re/sFKjcm
John Whitmer

2. “But everything we know about cognition suggests
that a small group of people, no matter how
intellingent, simply will not be smarter than the
larger group. ... Centralization is not the answer.
But aggregation is.”
- J. Surowiecki, The Wisdom of Crowds, 2004

3. Ambitous Outline
1. Situating Analytics
2. Academic Analytics
– Case Study: CSU Data Dashboard
3. Learner Analytics
– Case Study: CSU Chico
4. Promising Efforts & Resources
5. Q & A

4. SITUATING ANALYTICS

5. Steve Lohr, NY Times, August 5, 2009

6. Economist. (2010, 11/4/2010). Augmented business: Smart systems will disrupt lots of industries, and perhaps the entire
economy. The Economist.

7. Source: jisc_infonet @ Flickr.com
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Source: jisc_infonet @ Flickr.com

8. What’s the promise of Analytics for
Academic Technologists?
1. Decision-making (and service-evaluating)
based on practices (not just perceptions) and
performance outcomes
2. If we’re moving into a strategic role re: teaching and
learning, analytics can:
– demonstrate the link between technology and learning
– distinguish our role from a technology service provider
(PS - anyone else concerned about the validity of student
evaluations and self-reported data?)
– “Rate your level of technology expertise (novice,
intermediate, expert)”

9. Academic Analytics
“Academic Analytics marries large data sets with
statistical techniques and predictive modeling to
improve decision making”
(Campbell and Oblinger 2007, p. 3)

10. Academic Analytics
1. Term adopted in 2005 ELI research
report (Goldstein & Katz, 2005)
– Response to widespread adoption ERP
systems, desire to use data collected
for improved decision making
– 380 respondents; 65% planned to
increase capacity in near future
2. Call to move from
transactional/operational
reporting to what-if analysis,
predictive modeling, and alerts
3. LMS identified as potential domain
for future growth 10

11. CSU GRADUATION INITIATIVE
DATA DASHBOARD

12. CSU Graduation initiative
1. System Commitment to raise freshman
graduation rate 8% by 2015-2016
2. Cut achievement gap for under-represented
minority students by 50%
3. Each CSU campus created own plan &
activities to meet goals
More info: http://graduate.csuprojects.org/

13. DD Screenshot

14. Learner Analytics:
“ ... measurement, collection, analysis and
reporting of data about learners and their
contexts, for purposes of understanding and
optimizing learning and the environments in
which it occurs.” (Siemens, 2011)

15. Learner Analytics
1. Assess relationship between learning context (aka
educational technology usage) and student
learning and/or achievement
2. Most research to date: LMS for fullly online courses
3. More complex than Academic Analytics,
considering:
– Variation in LMS usage by course
– LMS learning actions are patterns, not clicks
– No significant difference literature: not what
technology used, it’s how it’s used, who uses it, and
for what purpose

16. Academic technologists have unique knowledge
to design and conduct learner analytics
(it’s our magic, a la Richard Katz!)
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17. CSU CHICO VISTA ANALYTICS
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18. 18

19. 19

20. 20

21. 21

22. 22

23. 23

24. Learner Analytics on Chico Vista Usage
1. What is the relationship between LMS usage and
student achievement?
2. What is the relationship between the number of LMS
tools used (aka ‘breadth of faculty LMS adoption’) and
student achievement?
3. Perform analysis within courses
4. Ultimate goal: provide administrators and faculty
with what-if modeling tools, building on reports in
data warehouse
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25. CSU Practice

27. Call to Action
1. Metrics reporting is the foundation for Analytics
2. Don’t need to wait for student performance
data; good metrics can inspire access to
performance data
3. You’re *not* behind the curve, this is a rapidly
emerging area that we can (should) lead ...

28. Promising Efforts & Directions
1. WCET “Predictive Analytics Framework”
(http://bit.ly/tMYFNF)
– Participants: American Public University System, Colorado CCS,
University of Hawaii System, University of Illinois at Springfield, Rio
Salado College, University of Phoenix
2. Building Organizational Capacity for Analytics Survey
(http://bit.ly/vPxKnw)
3. Educause Analytics “Capacity Building” initiative
(http://bit.ly/rLux6x)
Note: each of these efforts is supported by Linda Baer, Gates
Foundation

29. Resources to move forward with
Analytics at your campus
 Learner Analytics bibliography: http://bit.ly/rC0l5T
 Visualizing Data: Essential Collection of Resources:
http://bit.ly/sNriMe
 Moodle Custom SQL queries report:
http://bit.ly/toPWWD
 Bb Stats: http://bit.ly/w0L6th
 Bb Project Astro: http://bit.ly/w0L6th

30. Q&A and Contact Info
• Kathy Fernandes (kfernandes@csuchico.edu)
• Scott Kodai (skodai@csuchico.edu)
• John Whitmer (jwhitmer@csuchico.edu)
Download presentation at:
http://slidesha.re/sFKjcm
30

31. Works Cited
Arnold, K. E. (2010). Signals: Applying Academic Analytics. Educause Quarterly,
33(1).
California State University Office of the Chancellor. (2010). CSU Graduation
Initiative Retrieved 10/18, 2010, from http://graduate.csuprojects.org/
Campbell, J. P. (2007). Utilizing student data within the course management system
to determine undergraduate student academic success: An exploratory study.
Unpublished Ph.D., Educational Studies, United States -- Indiana.
Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic Analytics: A New
Tool for a New Era. EDUCAUSE Review, 42(4), 17.
Goldstein, P. J., & Katz, R. N. (2005). Academic analytics: The uses of management
information and technology in higher education. . Washington, DC.
Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an "early
warning system" for educators: A Proof of Concept. Computers &
Education(54), 11.
Offenstein, J., Moore, C., & Shulock, N. (2011). Advancing by Degrees: A Framework
for Increasing College Completion.
Siemens, G. (2011, 8/5). Learning and Academic Analytics.
http://www.learninganalytics.net/
Surowiecki, J. (2004). The Wisdom of Crowds. New York: Anchor Books.
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32. BONUS SLIDES!
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33. Academic Analytics Levels & Frequency
Level 1: Extraction
and reporting of
Analytics Level Respondents transaction-level data
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Level 1: Extraction and reporting of 6
7
17
transaction-level data 263 Level 2: Analysis and
monitoring of
Level 2: Analysis and monitoring of operational
operational performance 51 51 performance
Level 3: What-if decision support 6 Level 3: What-if
Level 4: Predictive decision support
Modeling/Simulation 7 263
Level 5: Automated triggers/alerts 17
Level 4: Predictive
N/A 32 Modeling/Simulation
Table and Chart adapted from Goldstein & Katz, 2005
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34. Research Findings
1. There is not a relationship between
sophistication of technology and
sophistication of application/deployment
– Largest raw number of advanced users had simple
transactional reporting tools
2. Factors leading to higher levels application:
– Leadership commitment to evidence-based decision
making
– Staff skills
– Effective end user training
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35. CSU GRADUATION INITIATIVE DATA
DASHBOARD
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36. Data Dashboard Theoretical
Framework & Guiding Questions
1. What percentage of
students reach each of the
leading indicators?
2. What is the impact of
reaching each of the leading
indicators on success rate?
3. Does meeting any of the
indicators reduce or
eliminate gaps between Advancing by Degrees: A Framework for Increasing
student groups? College Completion
-Institute for Higher Education Leadership and
Policy and The Education Trust
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37. DD Screenshot

38. DD Screenshot

39. EXAMPLES OF LEARNER ANALYTICS
RESEARCH
39

40. JP Campbell Dissertation Study (2007)
Utilizing student data within the course
management system to determine
undergraduate student academic success: An
exploratory study
1. LMS usage for entire university for 1 semester
(70,000 records, 27,000 students)
2. 15 demographic variables, 20 Vista variables
3. Outcome variable: student grade
4. Multivariate regression to create predictive
model for significant variables
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41. How much do Vista usage variables increase
predictive accuracy compared to predictions
based on student characteristics only?
a) 0.3%
b) 5%
c) 12%
d) 25%
e) 54%
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42. How much do Vista usage variables increase
predictive accuracy compared to predictions
based on student characteristics only?
a) 0.3%
b) 5%
c) 12% Prediction rate: 62.4%
d) 25%
e) 54%
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43. Why such a small increase?
1. Variation in usage creates “missing data” for
tools not used in other courses
2. Lesson Learned: perform analysis relative to
students within the same course
3. Next Generation implementation: Purdue
Biology course using “Signals” early warning
system with students (Arnold, 2010)
– D/F grades reduced 14%
– B/C grades increased 12%
43

44. Macfadyen and Dawson (2010)
In a fully online biology course at the University of British Columbia (n=118, 5
sections, 3 semesters), found that:
1. 33% of student grade variability could be explained by 3 variables
(discussion messages posted, mail messages sent, and assessments
completed)
2. 13 variables (out of 22 studied) had significant correlations with final
student grade (R2 values from .05 to .27)
– Significant variables included number online sessions, total time only, and
activities within content, mail, assessment, and discussion areas
– Variables not significant included some predictable items, such as visits to
MyGrades, uses of search, ‘who is online’, and the ‘compile’ tool. They also
included surprising items, such as the number of assignments read, the
time spent on assignments, and announcement views
3. 73.7% of the students correctly classified as at-risk (i.e. final grade of D
or F) through predictions based on these three variables
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