A Semi-Supervised Regression Algorithm for Grade Prediction of Students in Distance Learning Courses

Abstract

Applying data mining methods in the educational field has gained a lot of attention among researchers in recent years. Educational Data Mining has turned into an effective tool for uncovering hidden relationships in educational data and predicting students’ learning outcomes. Several supervised methods have been successfully applied with the purpose of identifying students at risk of failing or of predicting their academic performance. Recently, the implementation of Semi-Supervised Learning (SSL) methods in the educational process indicated their superiority over the supervised ones. SSL is an emerging subfield of machine learning seeking to effectively exploit a small pool of labeled examples together with a large pool of unlabeled ones. On this basis, a small number of students’ data from previous years may be used as the training set of a learning model to predict future outcomes of current students. A number of rewarding studies deal with the implementation of classification methods in the educational field in contrast to regression, which is deemed to be a slightly touched task. In this paper, a novel semi-supervised regression (SSR) algorithm is presented for predicting the final grade of undergraduate students in a distance online course. To the best of our knowledge there is no study dealing with the implementation of SSR methods in the educational field. A plethora of attributes related to students’ characteristics, academic performance and interaction within the course online platform form the training set, while several experiments were carried out confirming the superiority of the proposed algorithm over familiar regression methods. The experiment results show that the predictive performance of the proposed algorithm is increasing significantly over time, achieving a MAE value of less than 1.2358 before the middle of the academic year, which provides the advantage of early warnings and interventions.