Research plan on the effects of interventions on dropout predictions for higher education institutions

Abstract

One of the main challenges that Higher Education Institutions face currently is dropout/ student retention. In most cases, identifying this group of stu-dents is no easy task, and doing so on time is even harder. This challenge re-quires both speed and accuracy, which makes it a prime candidate for the use of machine learning models and predictions. We are currently developing a series of models capable of early identification of students at risk of dropping out, with one key difference from classic approaches: we want to not only find out who these students are, but how we can best help them avoid that prediction. By developing methodologies capable of identifying and measur-ing the effects of a series of interventions (academic guidance courses, extra-curricular encouragement, diminished course load, etc.), we intend to devel-op a system capable of providing counterfactuals (what the student needs to change or do to reverse a prediction) based on the causal effects of the previ-ously mentioned interventions. In this manner, we would not only identify groups of students at risk of dropping out, but would be doing so on time, and with a viable and specific strategy for each individual to improve.