Unifying the data; connect disparate data systems, data and initiatives to gain insight into what’s working & what’s not & for whom
The area that’s of most interest to Phil is the LMS, because that’s where we have the most interaction. Unfortunately, it’s mostly log file information. Scroll and click information is not captured. LTI integration does not help much because it’s based on an iFrame and we lose context. Instead, they’re using Adobe Analytics (formerly Omniture). We’re also using social sharing.
Institution-Specific Platform for Innovation
Unified Data Layer (Student Data Footprint – historic and incoming disparate systems) is connected to:
- Institution-Specific Deep Predictive Flow Models
- Frontline Apps & Initiatives
- Robust Testing and Measurement
Matthew then talked about the Civitas Learning Platform components (not exactly a sales pitch, but not too far off).
- UA Historical Overall Fall to Fall Retention Rate = 87%
- FTIC FT Freshman Historical Average First Year REtention Rate = 80%
- Prediction for Fall 2015 Cohort = 80%
- n=6,970 students
Data set used for modeling:
- Train: Fall 2012 to Fall 2013
- Test: Fall 2013 to Fall 2014
- AUC .844
- 90% accuracy
For FTFT Freshman in their First Term, students with SAT Math >550 persist at a rate +1- percentage points higher than SAT Math <550
For FTFT Freshman in their First Term, an LMS Course Grade on day 14 that is lower than 75% is associated with lower persistence than students with grades over 75%.
For transfer students overall, following course pathways traveled by students who graduated is beneficial for persistence.
For FTIC students who deviate significantly from the course pathway, the effect can be very bad.
Angela mentioned how useful the toolset is for the ability to see a list of students that make up any active filter segment within the too, and dig deeper on the their activity to extract additional actionable insights.