Predicting Students' GPA and Developing Intervention Strategies Based on Self-Regulatory Learning Behaviors

Predicting students' grades has emerged as a major area of investigation in education due to the desire to identify the underlying factors that influence academic performance. Because of limited success in predicting the grade point average (GPA), most of the prior research has focused on predicting grades in a specific set of classes based on students' prior performances. The issues associated with data-driven models of GPA prediction are further amplified by a small sample size and a relatively large dimensionality of observations in an experiment. In this paper, we utilize the state-of-the-art machine learning techniques to construct and validate a predictive model of GPA solely based on a set of self-regulatory learning behaviors determined in a relatively small-sample experiment. We quantify the predictability of each constituents of the constructed model and discuss its relevance. Ultimately, the goal of grade prediction in similar experiments is to use the constructed models for the design of intervention strategies aimed at helping students at risk of academic failure. In this regard, we lay the mathematical groundwork for defining and detecting probably helpful interventions using a probabilistic predictive model of GPA. We demonstrate the application of this framework by defining basic interventions and detecting those interventions that are probably helpful to students with a low GPA. The use of self-regulatory behaviors is warranted, because the proposed interventions can be easily practiced by students.