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Guest Editorial: Special Issue on Early Prediction and Supporting of Learning Performance

The papers in this special section focus on early prediction and the support of learning performance. Predicting student's learning performance in traditional face-to-face learning, online learning (LMS, MOOCs, etc.), and blended learning is a challenging but essential task in education [1]. On the one hand, it has become a difficult challenge due to the high number of factors that can influence a student’s final status. On the other hand, it is a critical issue in education because it concerns many students of all levels (primary education, secondary education, and tertiary or higher education) and institutions over the entire world. Moreover, also, an increase in the number of low performing students can cause a lower graduation rate, an inferior institution reputation in the eyes of all involved, and it usually results in overall financial loss. The task of predicting students’ performance is one of the oldest and most studied tasks in Educational Data Mining (EDM) and Learning Analytics (LA), and a wide range of classification and regression approaches have been successfully applied.