This systematic literature review investigates the fairness of machine learning algorithms in educational settings, focusing on recent studies and their proposed solutions to address biases. Applications analyzed include student dropout prediction, performance prediction, forum post classification, and recommender systems. We identify common…

The field of learning analytics (LA) has seen a gradual shift from purely data-driven approaches to more holistic views of improving student learning outcomes through data-informed learning design (LD). Despite the growing potential of LA in higher education (HE), the benefits are not yet convincing to the practitioner, in particular aspects of…