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System-wide LMS
  Learner Analytics Projects


Presenters: Kathy Fernandes and John Whitmer

            ATSC Virtual Meeting
             December 13, 2012
                                   Slides @
                                   http://goo.gl/DYqJU
Agenda
1. Chico State Learner Analytics Research Study
   •   EDUCAUSE Article (http://goo.gl/tESoi)

2. Current Projects
   •   Moodle
   •   Blackboard




                                                2
1. CHICO STATE LEARNER ANALYTICS
RESEARCH STUDY

“Logging on to Improve Achievement” by John Whitmer
EdD. Dissertation (UC Davis & Sonoma State)

                                               3
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
                                                     4
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?
                                                   5
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
Clear Trend: Grade w/Mean LMS Hits




                                 7
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
                                                 8
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
                                                 9
At-Risk Students: “Over-Working Gap”




                                  10
Filtering Data – Lots of “Noise”; Low “Signal”
                                                                                                         500


                                                                                                         450


                                                                                                         400


                                                                                                         350


                                                                                                         300


        382                                                                                              250


                                                                                                         200


                                                                                                         150   Raw Average
                               151                                                                             Hits/Student
                                                                                                         100

                                                                          49                                   Filtered Average
                                                    58                                                   50
                                                                                             26
                                                                                                               Hits/Student
         54                    51
                                                    23                    36
                                                                                             16
                                                                                                         0
 Discussion Activity   Content Activity Hits Assessment Activity   Mail Activity Hits   Administrative
        Hits                                       Hits                                  Activity Hits


 Final data set: 72,000 records (-73%)
                                                                                                                              11
2. CURRENT PROJECTS



                      12
Moodle and Bb Learner Analytics
What do these have in common?

• Multi-campus CSU groups discussing
  common analytics questions & query
  definitions




                                       13
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
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



                                               15
“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%


                                                                                                                    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
                                                 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%


                                                                                                                    17
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
                                                   18
Faculty LMS Adoption

How many faculty are using the LMS in one or
          more course sections?




                                               19
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
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


                                                   21
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.
                                                                 22
Student at a Glance




                      23
Instructor at a Glance




                         24
Dean Dashboard




                 25
Feedback? Questions?


Kathy Fernandes
(kfernandes@csuchico.edu)

John Whitmer
(jwhitmer@calstate.edu)




                                26

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LMS Learner Analytics Presentation

  • 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 2
  • 3. 1. CHICO STATE LEARNER ANALYTICS RESEARCH STUDY “Logging on to Improve Achievement” by John Whitmer EdD. Dissertation (UC Davis & Sonoma State) 3
  • 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 4
  • 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? 5
  • 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
  • 7. Clear Trend: Grade w/Mean LMS Hits 7
  • 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 8
  • 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 9
  • 11. Filtering Data – Lots of “Noise”; Low “Signal” 500 450 400 350 300 382 250 200 150 Raw Average 151 Hits/Student 100 49 Filtered Average 58 50 26 Hits/Student 54 51 23 36 16 0 Discussion Activity Content Activity Hits Assessment Activity Mail Activity Hits Administrative Hits Hits Activity Hits Final data set: 72,000 records (-73%) 11
  • 13. Moodle and Bb Learner Analytics What do these have in common? • Multi-campus CSU groups discussing common analytics questions & query definitions 13
  • 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 15
  • 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% 16
  • 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% 17
  • 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 18
  • 19. Faculty LMS Adoption How many faculty are using the LMS in one or more course sections? 19
  • 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 21
  • 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. 22
  • 23. Student at a Glance 23
  • 24. Instructor at a Glance 24

Notes de l'éditeur

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