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Learning Analytics Panel Session


      Panelists: Kathy Fernandes, James Frazee,
            Andrew Roderick & Deone Zell
              Moderator: John Whitmer

                   DET/CHE 2012
                 November 28, 2012
                                     D/L slides @ http://goo.gl/UgBYM
Introductions
Kathy Fernandes
Director of Academic Technology, Chico State University
Director of CSU System-wide LMSS Project, CSU Office of the Chancellor

James Frazee
Director, Instructional Technology Services, San Diego State University

Andrew Roderick
Technology Development Manager, San Francisco State University

Deone Zell
Senior Director, Academic Technology, California State University, Northridge

John Whitmer
Associate Director System-wide LMSS Project, CSU Office of the Chancellor


                                                            Slides: http://goo.gl/UgBYM
Outline
1. L.A. Context: Definition & Examples

2. Panelist Discussion

3. Audience Q & A




                                         Slides: http://goo.gl/UgBYM
Learning Analytics: Working Definition for
          Academic Technologists

 Analysis of the relationship between the use of
  academic technology and student achievement
  – Uses data, frequently “big data”, disaggregated
  – Builds on (and moves beyond) usage reporting
    to examine impact
  – Focused on making change

 Isn’t a yes/no dichotomy; better seen as a
  spectrum
                                        Slides: http://goo.gl/UgBYM
EXAMPLES OF LEARNING
ANALYTICS IN PRACTICE
Chico State Case Study: RELS 180

•   Redesigned to hybrid delivery
    through Academy eLearning           54 F’s

•   Enrollment: 373 students
    (54% increase on largest section)

•   Highest LMS 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
Correlation: LMS Use w/Final Grade


   Scatterplot of
Assessment Activity
  Hits vs. Course
       Grade



  Statistically Significant (strong to weak)     r    % Variance        Sign.
    Total Hits                                 0.48      23%           0.0000
    Assessment activity hits                   0.47      22%           0.0000
    Content activity hits                      0.41      17%           0.0000
    Engagement activity hits                   0.40      16%           0.0000
    Administrative activity hits               0.35      12%           0.0000
    Mean value all significant variables                18%
                                                            Slides: http://goo.gl/UgBYM
Correlation: Student Char. w/Final Grade


   Scatterplot of
 HS GPA vs. Course
      Grade



      Statistically Significant (strong to weak)     r     % Variance    Sign.
        HS GPA                                     0.31       9%        0.0000
        URM and Pell-Eligibility Interaction       -0.26      7%        0.0001
        Under-Represented Minority                 -0.21      4%        0.0001
        Enrollment Status                          0.19       3%        0.0003
        URM and Gender Interaction                 -0.15      2%        0.0033
        Pell Eligible                              -0.15      2%        0.0045
        First in Family to Attend College          -0.11      1%        0.0327
         Mean value all significant variables                 4%

      Not Statistically Significant
        Gender                                     0.10       1%        0.0557
        Major-College                              0.06       0%        0.2522   Slides: http://goo.gl/UgBYM
Most interesting finding (so far):

       LMS                      Student
       Use                   Characteristic
                               Variables

                       >
     Variables

        18%                         4%
  (Mean % change             (Mean % change
  explained by any           explained by any
single LMS variable)       single S.C. variable)


                                  Slides: http://goo.gl/UgBYM
SIGNALS


Purdue Signals Project   http://www.itap.purdue.edu/studio/signals/
Wordcloud of student evaluations of Course Signals (Arnold, 2010)
Signals Course Outcomes
 Compare same course (w/Signals v. w/o Signals)
  -6.41% DWF
  +10.97% A/B




                                         (Arnold, 2010)




                                    Slides: http://goo.gl/UgBYM
PANEL QUESTIONS
Q1: What strategic question(s) does your
campus hope to answer with Learning
Analytics?
Q2: What are some example(s) of
Learning Analytics that you have
conducted on your campus, no matter
how small the sample size? What’s
worked? What hasn’t? Why?
Q3: What should educational
technologists do to make progress
deploying Learning Analytics on their
campuses?
For More Information
 Learning Analytics Resources Googledoc:
  http://goo.gl/Fwur6

 Society for Learning Analytics Research:
  http://goo.gl/bH9ts

 Educause Learning Analytics Library:
  http://goo.gl/UDRMx


                                         Slides: http://goo.gl/UgBYM
AUDIENCE Q & A   By WingedWolf
                  Damián Navas
Contact Information
Kathy Fernandes (kfernandes@csuchico.edu)
James Frazee (jfrazee@mail.sdsu.edu)
Andrew Roderick (roderick@sfsu.edu)
Deone Zell (deone.zell@csun.edu)
John Whitmer (jwhitmer@calstate.edu)




                                 Slides: http://goo.gl/UgBYM

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Learner Analytics Panel Session: Deja-Vu all over again?

  • 1. Learning Analytics Panel Session Panelists: Kathy Fernandes, James Frazee, Andrew Roderick & Deone Zell Moderator: John Whitmer DET/CHE 2012 November 28, 2012 D/L slides @ http://goo.gl/UgBYM
  • 2. Introductions Kathy Fernandes Director of Academic Technology, Chico State University Director of CSU System-wide LMSS Project, CSU Office of the Chancellor James Frazee Director, Instructional Technology Services, San Diego State University Andrew Roderick Technology Development Manager, San Francisco State University Deone Zell Senior Director, Academic Technology, California State University, Northridge John Whitmer Associate Director System-wide LMSS Project, CSU Office of the Chancellor Slides: http://goo.gl/UgBYM
  • 3. Outline 1. L.A. Context: Definition & Examples 2. Panelist Discussion 3. Audience Q & A Slides: http://goo.gl/UgBYM
  • 4. Learning Analytics: Working Definition for Academic Technologists  Analysis of the relationship between the use of academic technology and student achievement – Uses data, frequently “big data”, disaggregated – Builds on (and moves beyond) usage reporting to examine impact – Focused on making change  Isn’t a yes/no dichotomy; better seen as a spectrum Slides: http://goo.gl/UgBYM
  • 6. Chico State Case Study: RELS 180 • Redesigned to hybrid delivery through Academy eLearning 54 F’s • Enrollment: 373 students (54% increase on largest section) • Highest LMS 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
  • 7. Correlation: LMS Use w/Final Grade Scatterplot of Assessment Activity Hits vs. Course Grade Statistically Significant (strong to weak) r % Variance Sign. Total Hits 0.48 23% 0.0000 Assessment activity hits 0.47 22% 0.0000 Content activity hits 0.41 17% 0.0000 Engagement activity hits 0.40 16% 0.0000 Administrative activity hits 0.35 12% 0.0000 Mean value all significant variables 18% Slides: http://goo.gl/UgBYM
  • 8. Correlation: Student Char. w/Final Grade Scatterplot of HS GPA vs. Course Grade Statistically Significant (strong to weak) r % Variance Sign. HS GPA 0.31 9% 0.0000 URM and Pell-Eligibility Interaction -0.26 7% 0.0001 Under-Represented Minority -0.21 4% 0.0001 Enrollment Status 0.19 3% 0.0003 URM and Gender Interaction -0.15 2% 0.0033 Pell Eligible -0.15 2% 0.0045 First in Family to Attend College -0.11 1% 0.0327 Mean value all significant variables 4% Not Statistically Significant Gender 0.10 1% 0.0557 Major-College 0.06 0% 0.2522 Slides: http://goo.gl/UgBYM
  • 9. Most interesting finding (so far): LMS Student Use Characteristic Variables > Variables 18% 4% (Mean % change (Mean % change explained by any explained by any single LMS variable) single S.C. variable) Slides: http://goo.gl/UgBYM
  • 10. SIGNALS Purdue Signals Project http://www.itap.purdue.edu/studio/signals/
  • 11. Wordcloud of student evaluations of Course Signals (Arnold, 2010)
  • 12. Signals Course Outcomes  Compare same course (w/Signals v. w/o Signals) -6.41% DWF +10.97% A/B (Arnold, 2010) Slides: http://goo.gl/UgBYM
  • 14. Q1: What strategic question(s) does your campus hope to answer with Learning Analytics?
  • 15. Q2: What are some example(s) of Learning Analytics that you have conducted on your campus, no matter how small the sample size? What’s worked? What hasn’t? Why?
  • 16. Q3: What should educational technologists do to make progress deploying Learning Analytics on their campuses?
  • 17. For More Information  Learning Analytics Resources Googledoc: http://goo.gl/Fwur6  Society for Learning Analytics Research: http://goo.gl/bH9ts  Educause Learning Analytics Library: http://goo.gl/UDRMx Slides: http://goo.gl/UgBYM
  • 18. AUDIENCE Q & A By WingedWolf Damián Navas
  • 19. Contact Information Kathy Fernandes (kfernandes@csuchico.edu) James Frazee (jfrazee@mail.sdsu.edu) Andrew Roderick (roderick@sfsu.edu) Deone Zell (deone.zell@csun.edu) John Whitmer (jwhitmer@calstate.edu) Slides: http://goo.gl/UgBYM

Editor's Notes

  1. Kathy
  2. John
  3. Kathy