Bb Tour ANZ 2017 - Predicting Student Success
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Introducing Blackboard Predict. Answering one critical question now.
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Bb Tour ANZ 2017 - Predicting Student Success
- 1. Chris Eske Analytics Specialist APAC Feb 2017 Predicting Student Success
- 2. Answering one Critical Question Now 2 • What is the likelihood that a student will achieve a C or better grade? • What is likelihood that a student will attend class next week?
- 3. Retention Rates in Australia 3 • University of Divinity 95.5% • XXXX University 73%
- 4. Student Success Return on Investment Calculator Current Full Time Student Numbers 25,000 Income per Student per annum $13,566* Current Retention Rate 95.00% Target Retention Rate 96.00% Increased Revenue $6,783,000 Note: Assumes two years of lost revenue per unsuccessful student *Derived from Higher Education Research, Facts and Figures November 2015 4
- 6. 6 Predictive modeling overview Problem What is the question you are trying to answer? Ingest Aggregate the data from multiple sources Modeling Turn the data into valuable info (answer the question) Distribute Put the valuable info in the hands of those who can act on it
- 7. 7 Blackboard Predict How it works • Aggregate student activity data • Present risk report to faculty/advisors Predictive Model • Current class activity from LMS • Past student information from SIS • Can include data from other sources: Portfolio, ebook/content, response systems LMS/ SIS Alerts & reports Activity Data analysis 1 2 4 3
- 8. 8 Sample model output Features and weights Week Zero Model Weekly Model .00 .05 .10 .15 Current GPA Course add weeks prior Transfer credits EFC Percent of classes passed Ethnicity Class load Academic year Course modality .00 .10 .20 .30 .40 Earned over attempted Number of page views Earned over possible Percent of classes passed Days since last page view Posts to faculty
- 9. 9 Who could intervene? Instructor Channel LMS – visualizations Message Provide triage/assistance to the students who are at risk Student Channel LMS – visualizations Message Compare to average; project grades Advisor/Counselor Channel CRM – tasks or Portal – visualizations Message These are your students who need the most help
- 10. 10 Faculty and students Advisors (with CRM) Advisors (Portal) Send results to the user
- 12. 12 Predict – a three month go live
Editor's Notes
- Fairly simplistic approach to processing data. MAJOR POINT: This solution is about intervention. The system will help to identify who is at risk. It’s up to the institution to act on it. We can’t make faculty/advisor intervene Thought experiment – if I gave you a list of 50 students who I KNOW are going to drop out next week, what would you do with that list? If the school doesn’t have a good answer to this question, the product might not be a good fit.
- Talk about the kind of data we get from the SIS And from the LMS Other sources can be used if they are “rich” enough (if there is enough usage). Usually, SIS and LMS are sufficient
- Sample from a past client Week Zero model is SIS data only. Notice the balanced contribution of features Weekly model is an aggregation of Week 1 – Week 16 models. Notice how current course grade (earned over attempted) outweighs other features Model is custom built for the institution based on their historical data. We build one model for each week (Week zero through next-to-last week)
- Match the message to the recipient
- We are aware of application fatigue. Don’t want to make the user log in to another system If they are already in the LMS/CRM, then put the info there…in front of them