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Predictive Modelling of Student Performance

Predictive Modelling of Student Performance. Project Objective. Help institutions answer these questions: Which students are most likely to attain the level of academic achievement expected? Who is at risk to under-perform? How effective are our support interventions?.

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Predictive Modelling of Student Performance

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  1. Predictive Modelling of Student Performance

  2. Project Objective Help institutions answer these questions: Which students are most likely to attain the level of academic achievement expected? Who is at risk to under-perform? How effective are our support interventions?

  3. Indicators come too late Mounds of data Mounds of decentralized data

  4. Approaches to Predicting Success/Risk Point-in-time assessments/surveys • Academic preparedness • Student attributes & attitudes Lagging indicators (attendance, grades, reports) Early warnings based on personal meetings and observations

  5. Jenzabar’s “Big Picture” Approach: Predictive Modelling LMS data SIS data Predictive Calculations & Risk Alarms Actionable Information External data Concerning Observations Data Warehouse Interventions Actionable Information

  6. Predictive Models for Distinct Populations • First-year • Transfers • High achievers • International • Upper-level • You name it…

  7. Predictive Model Example: First-year First-Year Traditional Predictive Group #2 Predictive Group #3…

  8. Comprehensive View SIS data Timetable & Lecturers Current & Historical Risk Assessment Current Risk Factor Values Alerts (manual & automated) Follow up Assigments Interventions

  9. Surprises in the Data First-generation often not as significant as expected Discipline increases success Highest achievers are higher risk We’re not collecting all the data we need!!

  10. Daily Reports

  11. Leverage Your Data to Improve Student Success

  12. Optional Slides

  13. Retention Data Universe All Retention Factors Significant Factors Predictive Factors

  14. Factor Analysis Process 30-70 5-12 • Standard List • School-Specific Identify Most Significant Factors Validate False Positives False Negatives

  15. Mining Your Data for Success Factors 3 (or more) Years Historical Data • Univariate analysis • Multi-variate analysis • Naïve-Bayes algorithms • Regression algorithms Tools: SSAS, R, Excel Models are evaluated for lift, accuracy and quality using BIDS

  16. Validating Your Predictive Models

  17. Jenzabar: A Platform to Improve Student Success

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