Recently, EducationNext released research on the predictive analytics that colleges and universities are using to identify at-risk students who may benefit from additional support. Excerpts from the piece appear below:
Colleges and universities are facing mounting pressure to raise completion rates and have embraced predictive analytics to identify which students are at risk of failing courses or dropping out. An estimated 1,400 institutions nationwide have invested in predictive analytics technology, with spending estimated in the hundreds of millions of dollars. Colleges and universities use these analyses to identify at-risk students who may benefit from additional support.
We put six predictive models to the test to gain a fuller understanding of how they work and the tradeoffs between simpler versus more complex approaches.
We find that complex machine-learning models aren’t necessarily better at predicting students’ future outcomes than simpler statistical techniques. The decisions analysts make about how they structure a data sample and which predictors they include are more critical to model performance. For instance, models perform better when we include predictors that measure students’ academic performance during a specific semester or term than when we include only cumulative measures of performance.
Out of 33,000 students, Ordinary Least Squares would correctly predict the graduation outcomes of 27,119, or 82 percent. Three models perform a bit better: Logistic Regression, XGBoost, and Recurrent Neural Networks. XGBoost is the best-performing model and would correctly predict graduation outcomes for 681 more students than Ordinary Least Squares, a 2.1 percent gain in accuracy.
Our findings raise important questions for institutions and policymakers about the value of investments in predictive analytics. Are institutions getting sufficient value from private analytics firms that market sophisticated models?