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Learning Analytics - Improving Student Retention

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Learning Analytics - Improving Student Retention

  1. 1. Learning Analytics – Improving Student Retention Paul Travill - Academic Registrar, University of Wolverhampton Chris Ballard - Innovation Consultant, TribalSITS:Vision Annual Conference
  2. 2. What is Learning Analytics? “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for the purpose of understanding and optimising learning and the environments in which it occurs.” (George Siemens 2011)SITS:Vision Annual Conference
  3. 3. What is Learning Analytics? Understand how students are Business Intelligence applied to learning and optimise the learning education at an institutional, process regional and national level Learning Academic Analytics Analytics Educational Data Predictive modelling Extract value from big data sets MiningSITS:Vision Annual Conference
  4. 4. Contrasting Learning and Academic Analytics Monitoring and benchmarking of university KPIsSITS:Vision Annual Conference
  5. 5. Education and Big DataSITS:Vision Annual Conference
  6. 6. Characteristics of Big Data Volume Variety Velocity 2011 Gartner ReportSITS:Vision Annual Conference
  7. 7. Factors affecting retention Preparation for higher education Academic Course Integration Demographic Social background Integration Personal Engagement circumstancesSITS:Vision Annual Conference
  8. 8. Engagement Administrative Data Activity Data and Academic Integration Academic Possible future performance at Course Enrolment Attendance VLE Usage data source entrance Student factors UCAS Application Fees Engagement Library Usage Contact with support Social background Module Grades Campus PC Usage services Demographics Proximity Contact with tutors Door access Social interaction Predictive ModelSITS:Vision Annual Conference
  9. 9. Administrative Activity Data Data • User interaction • Student with a system Initial Administration • Patterns of usage On going assessment of System • Real time assessment of risk • Known at time of • Collected at scale risk enrolment • Change over timeSITS:Vision Annual Conference
  10. 10. The dataset • Data Warehouse • Data set spanning 2010/11 – 2011/12 academic years • Imbalanced dataSITS:Vision Annual Conference
  11. 11. Cluster students Comparison to Student similar students Predict module Admin Data Module Outcome grades Model Retention Model Predict likelihood of Activity Data withdrawal View profile of Activity Profile Staff student interactions Which things can we change that could make a difference?SITS:Vision Annual Conference
  12. 12. SITS:Vision Annual Conference
  13. 13. Harry Potter Course: BWiz Quidditch NOTES FOR PERSONAL TUTOR Module: Basic Broomstick Skills Harry is likely to achieve a grade D. Issues: VLE use is very low compared with the better performing students, Distance from home suggests he should have a higher VLE use profile Support Recommendation: Suggest attendance at the additional Broomstick Study Skills sessions (Wednesday at 12.00 in the library) Click to make booking. Module: Quidditch magic spells Harry is likely to achieve a grade B. This matches the profile of other students in the profile cluster. Remember to encourage him to keep up the work!SITS:Vision Annual Conference
  14. 14. ACTIVITY DATASITS:Vision Annual Conference
  15. 15. Activity Data Student ID Date Time Asset ID Student ID Date Module Number of Transactions 0000001 01/09/2012 12:03:01 1 0000001 01/09/2012 Abc 2 0000001 01/09/2012 12:05:06 34 0000001 02/09/2012 Abc 0 0000001 05/09/2012 16:46:23 17 0000001 03/09/2012 Abc 0 0000005 17/10/2012 19:56:01 73 0000001 04/09/2012 Abc 0 … … … … 0000001 05/09/2012 Abc 1 0000005 17/09/2012 Bcd 7 … … … … Transactions Time SeriesSITS:Vision Annual Conference
  16. 16. Activity Data Goals • Convert to Time Series • Pre-process time series (e.g. smoothing) • Derive measures which describe the “shape” of the interactions • Use measures to help understand whether some patterns of interaction are indicative of poor engagementSITS:Vision Annual Conference
  17. 17. Extracting meaningful information from Activity Data • Need to distinguish between students who are regular users of the service, and those who have sporadic high volumes of access (but aggregate volume may be similar • Acts as a proxy to how well the student is “engaged” with the service is better than But may have similar overall numbers of transactionsSITS:Vision Annual Conference
  18. 18. Symbolic Aggregate approXimation (SAX) abcd bbbd eaad dbae Encodes the shape of the time series as a series of character strings Enables us to cluster together students with similar interaction patterns, or classify interactions (as indicative of students who ultimately withdrew) Turns out to work well for high volumes of interactions, but not so well for intermittent time series as there is less “shape” to encode. Smoothing the time series may helpSITS:Vision Annual Conference
  19. 19. Derive high level measures • Proportion of days accessed resource • Average number of transactions per day accessed • Run Length Distance Ratio Turns out to work better for Library and VLE activity data where interactions are much more intermittentSITS:Vision Annual Conference
  20. 20. School resource profiles • Low VLE usage does not mean the same thing for every student! • Need to weight Library and VLE features to take into account different resource profilesSITS:Vision Annual Conference
  21. 21. Challenges with activity data Averaged out measures of activity reduce the information available for each example A high proportion of students have no activity data available Comparing measures of activity over different time periods Inferring enough information before better predictive measures are availableSITS:Vision Annual Conference
  22. 22. BUILDING THE MODELSITS:Vision Annual Conference
  23. 23. Other Data Sets Active Other Data Sets SITS VLE Library Dir. New data Demographics, Enrolment, Activity Data, … 1. Train model 2. Test Predictions Data Transform Training examples (70%) model Warehouse Model Calculated Features Derived Features Test examples (30%)SITS:Vision Annual Conference
  24. 24. Performance of the model Lower Higher Performance Performance VLE Activity VLE Activity Library Activity Library Activity Admission Semester 1 Semester 2 • Demographics • Modules Failed • Modules Failed • Course • Modules • Modules • Academic Passed Passed performance • Credit points • Credit points on entry • DistanceSITS:Vision Annual Conference
  25. 25. Challenges with the model Imbalanced Its all about Training the Performance classes the training model with improves as require the examples examples the academic Imbalanced classes Performance Training data History need to and features! representing year adjust data the progress progresses to ensure the student model has makes at adequate different performance timesSITS:Vision Annual Conference
  26. 26. Chris Ballard Innovation Consultant, Tribal twitter: @chrisaballard blog: triballabs.netSITS:Vision Annual Conference

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