1. System-wide LMS
Learner Analytics Projects
Presenters: Kathy Fernandes and John Whitmer
ATSC Virtual Meeting
December 13, 2012
Slides @
http://goo.gl/DYqJU
2. Agenda
1. Chico State Learner Analytics Research Study
• EDUCAUSE Article (http://goo.gl/tESoi)
2. Current Projects
• Moodle
• Blackboard
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3. 1. CHICO STATE LEARNER ANALYTICS
RESEARCH STUDY
“Logging on to Improve Achievement” by John Whitmer
EdD. Dissertation (UC Davis & Sonoma State)
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4. Case Study: Intro to Religious Studies
• Redesigned to hybrid delivery through
Academy eLearning
54 F’s
• Enrollment: 373 students
(54% increase on largest section)
• Highest LMS (Vista) usage
entire campus Fall 2010
(>250k hits)
• Bimodal outcomes:
• 10% increased SLO mastery
• 7% & 11% increase in DWF
• Why? Can’t tell with aggregated
reporting data
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5. Driving Conceptual Questions
1. How is student LMS use related to academic
achievement in a single course section?
2. How does that finding compare to the relationship
of achievement with
traditional student characteristic variables?
3. How are these relationships different for
“at-risk” students (URM & Pell-eligible)?
4. What data sources, variables and methods are
most useful to answer these questions?
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6. Variables
Student Characteristic Independent Variables
Gender
Under Represented Minority (URM)
Pell-Eligible
High School GPA
First in Family to Attend College
Student Major (Discipline)
Enrollment Status
Interaction URM & Gender
Interaction URM & Pell-Eligibility
Learning Management System Usage Variables
Total LMS course website hits
Total LMS course dwell time
Administrative tool website hits
Assessment tool website hits
Content tool website hits
Engagement tool website hits
Dependent Variable: Final Course Grade 6
8. Separate Variables: Correlation LMS Use &
Student Characteristic with Final Grade
LMS Student
>
Use Characteristic
Variables Variables
18% Average 4% Average
(r = 0.35–0.48) (r = -0.11–0.31)
Explanation of change Explanation of change
in final grade in final grade
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9. Combined Variables: Regression Final Grade by
LMS Use & Student Characteristic Variables
LMS Student
>
Use Characteristic
Variables Variables
25% +10%
(r2=0.25) (r2=0.35)
Explanation of change Explanation of change
in final grade in final grade
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13. Moodle and Bb Learner Analytics
What do these have in common?
• Multi-campus CSU groups discussing
common analytics questions & query
definitions
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14. Moodle vs. Bb Learner Analytics
Moodle CIG (18 months old) Blackboard Learn Group
Chair: Andrew Roderick, SFSU (just starting)
CIG Chair: Terry Smith, CSUEB
DIY, adopt and evaluate Bb Learn Analytics product
available “off the shelf”;
solutions from other defined and integrated with
Moodlers Peoplesoft
Starting with technical Pre-built Reports and
reporting to build accurate Dashboards to ANYONE on
indicators of use campus (admin. or faculty if
2 rounds of data collection authenticated)
already completed and Charts available inside LMS
discussed for Faculty and Student
Views 14
15. Moodle Reporting & Analytics, Round 1
Prioritized Moodle Queries from S&PG
governance group
Focused on measures of adoption
(% faculty, % students, % course sections)
For expediency, campuses reported using
current queries used for reporting
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16. “How many sections are using the LMS
(out of all sections offered that term)?”
CSU_09 671 2,191
CSU_08 1,098 1,162
CSU_06 2,997 7,064
Active Sections
Inactive Sections
CSU_05 2,492 3,687
CSU_04 553 614
CSU_02 2,270 3,911
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
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17. “How many sections are using the LMS
(out of all sections offered that term)?”
CSU_09 671 2,191
CSU_08 1,098 1,162
CSU_06 2,997
Use = “visible”+”student activity”
7,064
Active Sections
Inactive Sections
CSU_05 2,492 3,687
CSU_04 553
Use = “visible”
614
CSU_02 2,270 3,911
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
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18. Round 2: mCURL
(Moodle Common Usage and Learning Analytics)
8 active CSU & 2 UC campuses
– Co-chaired: John Whitmer, CO ATS and
Mike Haskell, Cal Poly SLO
Starting with same measures of adoption,
prioritizing “wish list” of more advanced analytics
Local database conventions and campus
practices make accurate comps. challenging
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20. mCURL Next Steps
Refine queries for accurate comparative course
and student adoption measures
Select additional queries: depth and breadth of
use
– # tools used
– # students in each section
– frequency of use
Create repositories for campuses to share
unique local queries 20
21. Blackboard Analytics for Learn (A4L)
CSU ATS Co-Lab Agreement – working together
– Functionality: from early alerts/course reporting
to institutional-level analytics
– Up to 4 campuses participating (3 confirmed)
– Period: December 2012-December 2013
– Individual campus Scope of Work for setup of
infrastructure and services
Kick-off meeting next week
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22. Co-Lab Goals
1. Develop methodologies and processes to identify, aggregate,
and transform LMS usage data into information for analytics.
2. Improve campus usage of learning analytics for decision-
making for student success, curriculum improvement, and technical
services.
3. Create shared measures, database reports, and algorithms,
drawing on campus best practices and research innovations.
4. Increase campus awareness of applications and technical tools.
5. Document campus efforts and disseminate to other campuses.
6. Provide professional development in learning analytics.
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