Generating Actionable Predictive Models of Academic Performance
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The document discusses the development of predictive models for academic performance using detailed data footprints and sophisticated algorithms. It emphasizes the importance of deriving actionable insights from these models to facilitate interventions for at-risk students. Key components include data indicators closely tied to learning design and a straightforward delivery method for feedback to instructors.
Generating Actionable Predictive Models of Academic Performance
- 1. Abelardo Pardo, Negin Mirriahi, Roberto Martínez-Maldonado, Jelena Jovanovic, Shane Dawson, Dragan Gašević KannanBflickr.com Generating Actionable Predictive Models of Academic Performance International Conference on Learning Analytics and Knowledge University of Edinburgh 29 April 2016
- 2. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. The problem • Detailed data footprints collected • Sophisticated algorithms applied • Predictive models created • How to derive/apply actions? 2 MichaelPereckasflickr.com
- 3. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. Retention/Attrition 3 TrevorHuxmanflickr.com Predict student abandoning course/institution E.g., Jayaprakash, S. M., Moody, E. W., Eitel, J. M., Regan, J. R., & Baron, J. D. (2014). Early Alert of Academically At-Risk Students : An Open Source Analytics Initiative. Journal of Learning Analytics, 1, 6-47.
- 4. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. Sophisticated predictive models 4 KevLewisflickr.com Romero, C., López, M.-I., Luna, J.-M., & Ventura, S. (2013). Predicting students' final performance from participation in on-line discussion forums. Computers & Education, 68, 458-472. doi:10.1016/j.compedu.2013.06.009 Classification • Divide students in groups • Useful for instructors • Unclear how to intervene
- 5. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. 5 Tanes, Z., Arnold, K. E., King, A. S., & Remnet, M. A. (2011). Using Signals for appropriate feedback: Perceptions and practices. Computers & Education, 57(4), 2414-2422. doi:10.1016/j.compedu.2011.05.016 Course Performance • Well • Mediocre • Poor VitBrunnerFlickr.com
- 6. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. Disproportionate attention 6 FarrukhFlickr.com Intervene Wise, A. F. (2014). Designing pedagogical interventions to support student use of learning analytics. Paper presented at the International Conference on Learning Analytics and Knowledge.
- 7. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. 7 Gather data on the state of the student Identify action to take Deliver feedback McKay, T., Miller, K., & Tritz, J. (2012). What to do with actionable intelligence: E2Coach as an intervention engine. Paper presented at the International Conference on Learning Analytics and Knowledge, Vancouver, BC, Canada. Paulflickr.com
- 8. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. Objective 1. Data indicators close to learning design 2. Predictive model 3. Bridge between model and application 4. Straightforward delivery method 8 OliverBraubachflickr.com
- 9. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. • Event counts from interactive course material • Midterm/final exam scores • Recursive partitioning • Divide cohort into performance categories 9 LouishPixelflickr.com
- 10. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. Recursive Partitioning • Arbitrary magnitudes in factors • Handle large number of factors • Handle heterogeneous factos • Model with intuitive interpretation • Performance? 10 theilrflickr.com
- 11. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. 11 WilliamMurphyflickr.com • 13 Week first year Engineering • Weekly activities (formative/summative) • Videos, MCQ, Exercises, dashboard • n = 272, Weeks 2-5 and 7-13
- 12. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. 12 Data collected • Indicators are directly connected with learning design • Data structure shaped by the schedule (weeks) • Data available in a per-week basis • What is the expected midterm/final score in week n?
- 13. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. Result Example • Week 10 • Predicted score at leaves (out of 40) • Conditions at nodes • If (EXC.in >=22) and (VID.PL < 8.5) then score = 6 13
- 14. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. • Each leaf node represents a group of students with their estimated score. • Example: 6, 8.3, 8.4, 9.4, 9.9, 10, 15 (out of 40) • Intervention: suggested work before exam 14 Result interpretation
- 15. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. 15 shabnammayetFlickr.com Performance RMSE: Root mean square error, MAE: Mean absolute error
- 16. Generating Actionable Predictive Models of Academic PerformancePardo, Mirriahi, et al. Conclusions and Future Work Indicators closed to learning design Hierarchical partition Student partition respect to midterm/final Acceptable performance Immediate action by instructors 16 HamishIrvineflickr.com
- 17. Abelardo Pardo, Negin Mirriahi, Roberto Martínez-Maldonado, Jelena Jovanovic, Shane Dawson, Dragan Gašević KannanBflickr.com Generating Actionable Predictive Models of Academic Performance International Conference on Learning Analytics and Knowledge University of Edinburgh 29 April 2016