Fuzzy-based active learning for predicting student academic performance using autoML: a step-wise approach

Abstract

Predicting students’ learning outcomes is one of the main topics of interest in the area of Educational Data Mining and Learning Analytics. To this end, a plethora of machine learning methods has been successfully applied for solving a variety of predictive problems. However, it is of utmost importance for both educators and data scientists to develop accurate learning models at low cost. Fuzzy logic constitutes an appropriate approach for building models of high performance and transparency. In addition, active learning reduces both the time and cost of labeling effort, by exploiting a small set of labeled data along with a large set of unlabeled data in the most efficient way. In addition, choosing the proper method for a given problem formulation and configuring the optimal parameter setting is a demanding task, considering the high-dimensional input space and the complexity of machine learning algorithms. As such, exploring the potential of automated machine learning (autoML) strategies from the perspective of machine learning adeptness is important. In this context, the present study introduces a fuzzy-based active learning method for predicting students’ academic performance which combines, in a modular way, autoML practices. A lot of experiments was carried out, revealing the efficiency of the proposed method for the accurate prediction of students at risk of failure. These insights may have the potential to support the learning experience and be useful the wider science of learning.