Need for Interpretable Student Performance Prediction

The education domain is growing at an exponential rate, maturing with the introduction of innovative and improved offerings to the learning community aided by various Education Data Mining techniques (EDM). Significant amount of research is being carried out around EDM, using various dimensions like student's performance, dropout rates, individual cognitive capabilities, teacher's, administrators' performance, course delivery, and content authoring. Several data analytics techniques are adopted to gain knowledge from educational data. However, the prediction models generated are complex and not interpretable in understanding why and how a prediction has arrived at from the produced results. The interpretability of the model becomes increasingly important when dealing with large datasets and complex models. The derived analytics and the models will be acceptable and trusted if they are interpretable. It helps the educational organizations to advance their learning processes in boosting students' performance and reduction in the student dropout rate. This study reviews EDM focusing on the factors influencing student's predictions, various algorithms used, and identified the gaps. The study also gives an insight into how the "black-box" decisions of the prediction model are made, the role of various eXplainable AI (XAI) techniques in making the model results interpretable, and their contribution to producing explainable results.