Research Plan on the Effects of Interventions on Dropout Predictions for Higher Education Institutions

One of the main challenges that Higher Education Institutions face currently is dropout/ student retention. In most cases, identifying this group of students is no easy task, and doing so on time is even harder. This challenge requires 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 measuring the effects of a series of interventions (academic guidance courses, extra-curricular encouragement, diminished course load, etc.), we intend to develop 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 previously 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. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

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