Hyperparameter Optimization and Comparison of Student Performance Prediction Algorithms

Educational Data Mining is the process of extracting information from datasets with educational relevance. This process of information extraction can be extremely insightful and useful for policy decision making but also small scale interventions. In our research, we optimize model configurations and hyperparameters of student performance prediction pipelines. Our models target to predict student performance based on data gathered in Portuguese schools pertaining to the subjects of Mathematics and Portuguese. The target variable is either cast in two or five bins and we train separate models for each of the tasks. We search model configurations amongst three different feature selection algorithms and 7 different classifiers implemented in scikit-learn. Our search spans 2000 iterations overall different experiment setup permutations. We successfully develop novel model configurations that perform exceptionally well. Furthermore, we set an important precedent showing the utility of hyperparameter selection and model search for Educational Data Mining.