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First steps with learning analytics
 CETIS 2013
 Birmingham | Mar 2013



  Professor Mark Stubbs
  Head of Learning & Research Technologies

  m.stubbs@mmu.ac.uk http://twitter.com/thestubbs
  http://slideshare.net/markstubbs

Tuesday, March 12, 2013                             1
Analytics to develop understanding
                                          2. Identify
                     1.Appreciate
                                        relevant data
                       the issue
                                           sources




                                 Refining
                                                        3.Summarise
       6.Analyse &
         visualise
                              understanding               individual
                                                        data sources
                               of a problem
                                   space


                      5 Prep the          4 Join on
                       data for           common
                       analysis          identifiers
Analytics case study one
Understanding impacts of a new VLE on student success
• PhD Student (Julie Hardman) led evaluation strand for
  MMU’s (WebCT Vista) VLE project from 2006-2009
• Stakeholders wanted to understand impacts of new VLE
• Suspicion that some patterns of VLE use were more or
  less likely to produce successful outcomes for students
• Lots of small-scale studies, but
  lack of institutional-scale research
Analytics case study one
Appreciation of                                  2. Identify                  Student
                            1.Appreciate
                                               relevant data
more and less                 the issue                                       demographics
                                                  sources
successful                                                                    VLE logs
patterns of VLE                                                               Exam board
use                                                                           outcomes


                                    Understanding
                                                               3.Summarise
              6.Analyse &
                visualise
                                    impacts of new               individual
                                                               data sources
                                    VLE on student                               Categorise
                                                                                 VLE usage
                                       success                                   & count hits
                                                                                 by category


                             5 Prep the          4 Join on
                              data for           common
                              analysis          identifiers
3.Summarise
                                                  individual
                                                data sources

Analytics case study one
WebCT Vista kept a detailed click-track log (Oracle DB)
• Custom SQL generated 1-row-per-student summaries:
   –   Number of areas student enrolled on & number active on
   –   Number of staff actively associated with those areas
   –   Days from start of term when VLE first & last used
   –   Percentage of total hits between 9am – 9pm
   –   Total number of student logons (distinct sessions)
   –   Total number of document hits by student & by tutors
   –   Total number of chat hits by student & by tutors
   –   Total number of assessment hits by student & by tutors
Analytics case study one
Appreciation of                                  2. Identify                  Student
                            1.Appreciate
                                               relevant data
more and less                 the issue                                       demographics
                                                  sources
successful                                                                    VLE logs
patterns of VLE                                                               Exam board
use                                                                           outcomes


                                    Understanding
                                                               3.Summarise
              6.Analyse &
                visualise
                                    impacts of new               individual
                                                               data sources
                                    VLE on student                               Categorise
                                                                                 VLE usage
                                       success                                   & count hits
                                                                                 by category


                             5 Prep the          4 Join on
                              data for           common
                              analysis          identifiers
                                                                       Join on
                                                                       student ID
4 Join on
                                                common
                                               identifiers

Analytics case study one
StudentID Demographics    a

StudentID Categorised VLE usage            b

StudentID Progressed?     c

    SELECT …
    FROM a, b, c
    WHERE a.StudentID = b.StudentID
    AND b.StudentID = c.StudentID


StudentID Demographics Categorised VLE usage      Progressed?
Analytics case study one
 Appreciation of                                    2. Identify                  Student
                               1.Appreciate
                                                  relevant data
 more and less                   the issue                                       demographics
                                                     sources
 successful                                                                      VLE logs
 patterns of VLE                                                                 Exam board
 use                                                                             outcomes


                                       Understanding
                                                                  3.Summarise
                 6.Analyse &
                   visualise
                                       impacts of new               individual
Interpret                                                         data sources
output of                              VLE on student                               Categorise
                                                                                    VLE usage
statistical                               success                                   & count hits
tests
                                                                                    by category


                                5 Prep the          4 Join on
                                 data for           common
      Code missing               analysis          identifiers
                                                                          Join on
      values, collapse                                                    student ID
      categories…
6.Analyse &
                                                             visualise

Analytics case study one
Random forest analysis of factors predicting progression:
• Percentage of student usage between 9am and 9pm
• Number of day since start of term when VLE last used
• Total number of tutors’ document hits
• Total number of students’ document hits
• Total number of tutors’ chat hits
• Total number of student logons (distinct sessions)
• Number of days since start of term when VLE first used



http://onlinelibrary.wiley.com/doi/10.1002/sres.2130/abstract
6.Analyse &
                                                             visualise

Analytics case study one
Visualising partial dependence on the top three predictors




    9am-9pm usage             Last VLE access             Tutors’ doc hits




http://onlinelibrary.wiley.com/doi/10.1002/sres.2130/abstract
Analytics case study one
 Appreciation of                                    2. Identify                  Student
                               1.Appreciate
                                                  relevant data
 more and less                   the issue                                       demographics
                                                     sources
 successful                                                                      VLE logs
 patterns of VLE                                                                 Exam board
 use                                                                             outcomes


                                       Understanding
                                                                  3.Summarise
                 6.Analyse &
                   visualise
                                       impacts of new               individual
Interpret                                                         data sources
output of                              VLE on student                               Categorise
                                                                                    VLE usage
statistical                               success                                   & count hits
tests
                                                                                    by category


                                5 Prep the          4 Join on
                                 data for           common
      Code missing               analysis          identifiers
                                                                          Join on
      values, collapse                                                    student ID
      categories…
1.Appreciate
                                                        the issue

Analytics case study one
Enhanced appreciation of relationships between VLE use and
student success
• “At risk of failure” alarm bells
   – High usage percentage outside 9am-9pm
   – Early finish or late start in year for VLE use
• Categorised VLE use more informative than total hits
   – Documents / Content
   – Chat / Dialogue
   – Assessment
• More questions than answers … we’ll be exploring further
  with our new Moodle VLE
1.Appreciate
                                                                    the issue

Analytics case study two
Understanding predictors of NSS overall satisfaction for
Science and Engineering courses
• Fielding, A.F., P.J.Dunleavy and A.M. Langan (2010)
  Effective use of the UK's National Student (Satisfaction)
  Survey (NSS) data in science and engineering subjects.
  Journal of Further and Higher Education, 33, 347-368.
• Concern that focusing on low mean scores without reference
  to subject benchmarks could be ineffective for improving
  overall satisfaction
More background…
•   http://www.gees.ac.uk/events/2010/feedbknss/documents/MarkLanganInsightsintoNSS_La
    ngan_GEES_Nov2010_submitted.ppt
Analytics case study two
 Appreciation                                         2. Identify                  UK NSS
                                1.Appreciate
                                                    relevant data
 of how best to                   the issue                                        dataset filtered
                                                       sources
 improve                                                                           by JACS3
 satisfaction                                                                      Science &
                                                                                   Engineering
                                        Understanding
                                          predictors
                                        satisfaction on             3.Summarise
                  6.Analyse &
                                                                      individual
Interpret           visualise
                                          Science &                 data sources       Question
output of
                                         Engineering                                   responses
statistical
                                                                                       summarised
tests
                                           courses                                     to %satisfied


                                 5 Prep the           4 Join on
                                  data for            common
      Code missing                analysis           identifiers
                                                                            Not required:
      values, collapse                                                      single dataset
      categories…
6.Analyse &
                                                                                    visualise

Analytics case study two
Random forest analysis of Q.s predicting overall satisfaction:
Rank   Predicting questionnaire item                                                  Inc MSE (%)
1      Q15 - The course is well organised and is running smoothly                        119.89
2      Q1 - Staff are good at explaining things                                           71.45
3      Q4 - The course is intellectually stimulating                                      66.71
4      Q14 - Any changes in the course or teaching have been communicated effectively     60.79
5      Q10 - I have received sufficient advice and support with my studies                55.34
…
10     Subject                                                                                  32.35
…
18     Q7 - Feedback on my work has been prompt                                                 10.49
19     Q9 - Feedback on my work has helped me clarify things I did not understand               6.65
20     Q5 - The criteria used in marking have been clear in advance                             6.60
21     Q21 - As a result of the course, I feel confident in tackling unfamiliar problems        3.32
22     Q8 - I have received detailed comments on my work                                        3.04
6.Analyse &
                                                               visualise

Analytics case study two
Sector predictions of Q22 from Q1-21 responses for Sci Eng
Under-performing…        Actual   Predicted   Residual SE1    SE2     SE3   Subjects




Over-performing…
Analytics case study two
 Appreciation                                         2. Identify                  UK NSS
                                1.Appreciate
                                                    relevant data
 of how best to                   the issue                                        dataset filtered
                                                       sources
 improve                                                                           by JACS3
 satisfaction                                                                      Science &
                                                                                   Engineering
                                        Understanding
                                          predictors
                                        satisfaction on             3.Summarise
                  6.Analyse &
                                                                      individual
Interpret           visualise
                                          Science &                 data sources       Question
output of
                                         Engineering                                   responses
statistical
                                                                                       summarised
tests
                                           courses                                     to %satisfied


                                 5 Prep the           4 Join on
                                  data for            common
      Code missing                analysis           identifiers
                                                                            Not required:
      values, collapse                                                      single dataset
      categories…
1.Appreciate
                                                    the issue

Analytics case study two
Enhanced understanding of the predictors of satisfaction for
Science and Engineering courses
• Statistical demonstration of significance of subject variation
  led to a more sophisticated NSS response at MMU
• Course organisation and teaching are significant predictors
• Low feedback scores not a significant predictor
Analytics to develop understanding
                                          2. Identify
                     1.Appreciate
                                        relevant data
                       the issue
                                           sources




                                 Refining
                                                        3.Summarise
       6.Analyse &
         visualise
                              understanding               individual
                                                        data sources
                               of a problem
                                   space


                      5 Prep the          4 Join on
                       data for           common
                       analysis          identifiers
Next steps with relevant data sources


                                                             Exam Board
 VLE logs                                                     Outcomes

                          Student & Course
                              Records

  Assignment                                                   Student
  Submissions                                                Satisfaction
                  2. Identify   3.Summarise     4 Join on
                relevant data     individual    common
                   sources      data sources   identifiers

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LRT Talks 2013-03-12 CETIS

  • 1. First steps with learning analytics CETIS 2013 Birmingham | Mar 2013 Professor Mark Stubbs Head of Learning & Research Technologies m.stubbs@mmu.ac.uk http://twitter.com/thestubbs http://slideshare.net/markstubbs Tuesday, March 12, 2013 1
  • 2. Analytics to develop understanding 2. Identify 1.Appreciate relevant data the issue sources Refining 3.Summarise 6.Analyse & visualise understanding individual data sources of a problem space 5 Prep the 4 Join on data for common analysis identifiers
  • 3. Analytics case study one Understanding impacts of a new VLE on student success • PhD Student (Julie Hardman) led evaluation strand for MMU’s (WebCT Vista) VLE project from 2006-2009 • Stakeholders wanted to understand impacts of new VLE • Suspicion that some patterns of VLE use were more or less likely to produce successful outcomes for students • Lots of small-scale studies, but lack of institutional-scale research
  • 4. Analytics case study one Appreciation of 2. Identify Student 1.Appreciate relevant data more and less the issue demographics sources successful VLE logs patterns of VLE Exam board use outcomes Understanding 3.Summarise 6.Analyse & visualise impacts of new individual data sources VLE on student Categorise VLE usage success & count hits by category 5 Prep the 4 Join on data for common analysis identifiers
  • 5. 3.Summarise individual data sources Analytics case study one WebCT Vista kept a detailed click-track log (Oracle DB) • Custom SQL generated 1-row-per-student summaries: – Number of areas student enrolled on & number active on – Number of staff actively associated with those areas – Days from start of term when VLE first & last used – Percentage of total hits between 9am – 9pm – Total number of student logons (distinct sessions) – Total number of document hits by student & by tutors – Total number of chat hits by student & by tutors – Total number of assessment hits by student & by tutors
  • 6. Analytics case study one Appreciation of 2. Identify Student 1.Appreciate relevant data more and less the issue demographics sources successful VLE logs patterns of VLE Exam board use outcomes Understanding 3.Summarise 6.Analyse & visualise impacts of new individual data sources VLE on student Categorise VLE usage success & count hits by category 5 Prep the 4 Join on data for common analysis identifiers Join on student ID
  • 7. 4 Join on common identifiers Analytics case study one StudentID Demographics a StudentID Categorised VLE usage b StudentID Progressed? c SELECT … FROM a, b, c WHERE a.StudentID = b.StudentID AND b.StudentID = c.StudentID StudentID Demographics Categorised VLE usage Progressed?
  • 8. Analytics case study one Appreciation of 2. Identify Student 1.Appreciate relevant data more and less the issue demographics sources successful VLE logs patterns of VLE Exam board use outcomes Understanding 3.Summarise 6.Analyse & visualise impacts of new individual Interpret data sources output of VLE on student Categorise VLE usage statistical success & count hits tests by category 5 Prep the 4 Join on data for common Code missing analysis identifiers Join on values, collapse student ID categories…
  • 9. 6.Analyse & visualise Analytics case study one Random forest analysis of factors predicting progression: • Percentage of student usage between 9am and 9pm • Number of day since start of term when VLE last used • Total number of tutors’ document hits • Total number of students’ document hits • Total number of tutors’ chat hits • Total number of student logons (distinct sessions) • Number of days since start of term when VLE first used http://onlinelibrary.wiley.com/doi/10.1002/sres.2130/abstract
  • 10. 6.Analyse & visualise Analytics case study one Visualising partial dependence on the top three predictors 9am-9pm usage Last VLE access Tutors’ doc hits http://onlinelibrary.wiley.com/doi/10.1002/sres.2130/abstract
  • 11. Analytics case study one Appreciation of 2. Identify Student 1.Appreciate relevant data more and less the issue demographics sources successful VLE logs patterns of VLE Exam board use outcomes Understanding 3.Summarise 6.Analyse & visualise impacts of new individual Interpret data sources output of VLE on student Categorise VLE usage statistical success & count hits tests by category 5 Prep the 4 Join on data for common Code missing analysis identifiers Join on values, collapse student ID categories…
  • 12. 1.Appreciate the issue Analytics case study one Enhanced appreciation of relationships between VLE use and student success • “At risk of failure” alarm bells – High usage percentage outside 9am-9pm – Early finish or late start in year for VLE use • Categorised VLE use more informative than total hits – Documents / Content – Chat / Dialogue – Assessment • More questions than answers … we’ll be exploring further with our new Moodle VLE
  • 13. 1.Appreciate the issue Analytics case study two Understanding predictors of NSS overall satisfaction for Science and Engineering courses • Fielding, A.F., P.J.Dunleavy and A.M. Langan (2010) Effective use of the UK's National Student (Satisfaction) Survey (NSS) data in science and engineering subjects. Journal of Further and Higher Education, 33, 347-368. • Concern that focusing on low mean scores without reference to subject benchmarks could be ineffective for improving overall satisfaction More background… • http://www.gees.ac.uk/events/2010/feedbknss/documents/MarkLanganInsightsintoNSS_La ngan_GEES_Nov2010_submitted.ppt
  • 14. Analytics case study two Appreciation 2. Identify UK NSS 1.Appreciate relevant data of how best to the issue dataset filtered sources improve by JACS3 satisfaction Science & Engineering Understanding predictors satisfaction on 3.Summarise 6.Analyse & individual Interpret visualise Science & data sources Question output of Engineering responses statistical summarised tests courses to %satisfied 5 Prep the 4 Join on data for common Code missing analysis identifiers Not required: values, collapse single dataset categories…
  • 15. 6.Analyse & visualise Analytics case study two Random forest analysis of Q.s predicting overall satisfaction: Rank Predicting questionnaire item Inc MSE (%) 1 Q15 - The course is well organised and is running smoothly 119.89 2 Q1 - Staff are good at explaining things 71.45 3 Q4 - The course is intellectually stimulating 66.71 4 Q14 - Any changes in the course or teaching have been communicated effectively 60.79 5 Q10 - I have received sufficient advice and support with my studies 55.34 … 10 Subject 32.35 … 18 Q7 - Feedback on my work has been prompt 10.49 19 Q9 - Feedback on my work has helped me clarify things I did not understand 6.65 20 Q5 - The criteria used in marking have been clear in advance 6.60 21 Q21 - As a result of the course, I feel confident in tackling unfamiliar problems 3.32 22 Q8 - I have received detailed comments on my work 3.04
  • 16. 6.Analyse & visualise Analytics case study two Sector predictions of Q22 from Q1-21 responses for Sci Eng Under-performing… Actual Predicted Residual SE1 SE2 SE3 Subjects Over-performing…
  • 17. Analytics case study two Appreciation 2. Identify UK NSS 1.Appreciate relevant data of how best to the issue dataset filtered sources improve by JACS3 satisfaction Science & Engineering Understanding predictors satisfaction on 3.Summarise 6.Analyse & individual Interpret visualise Science & data sources Question output of Engineering responses statistical summarised tests courses to %satisfied 5 Prep the 4 Join on data for common Code missing analysis identifiers Not required: values, collapse single dataset categories…
  • 18. 1.Appreciate the issue Analytics case study two Enhanced understanding of the predictors of satisfaction for Science and Engineering courses • Statistical demonstration of significance of subject variation led to a more sophisticated NSS response at MMU • Course organisation and teaching are significant predictors • Low feedback scores not a significant predictor
  • 19. Analytics to develop understanding 2. Identify 1.Appreciate relevant data the issue sources Refining 3.Summarise 6.Analyse & visualise understanding individual data sources of a problem space 5 Prep the 4 Join on data for common analysis identifiers
  • 20. Next steps with relevant data sources Exam Board VLE logs Outcomes Student & Course Records Assignment Student Submissions Satisfaction 2. Identify 3.Summarise 4 Join on relevant data individual common sources data sources identifiers