In this paper, we propose a counterfactual explanations-based approach to provide an automatic and intelligent recommendation that supports student's self-regulation of learning in a data-driven manner, aiming to improve their performance in courses. Existing work under the fields of learning analytics and AI in education predict students' performance and use the prediction outcome as feedback without explaining the reasons behind the prediction. Our proposed approach developed an algorithm that explains the root causes behind student's performance decline and generates data-driven recommendations for action. The effectiveness of the proposed predictive model that constitutes the intelligent recommendations is evaluated, with results demonstrating high accuracy.

The advancements in learning analytics and artificial intelligence have shown potential to transform traditional modalities of education. One such advancement relates to the use of educational data to track students’ knowledge state [1] . In the field of Artificial Intelligence in Education knowledge tracing is a well-established area where a machine models the students’ knowledge as they interact with coursework. Effective modeling of student knowledge can have a high impact on the provision of adaptive learning. In fact, lately, research on knowledge tracing is intensifying with a particular focus on the utilisation of new machine learning algorithms for modelling the students’ knowledge levels and for the prediction of performance on future tasks and assessment questions [2] . In the case of question-level assessment, knowledge tracing provides an interpretation of the learner’s current knowledge level and models their mastery of the skill or knowledge component to which future questions are related [3] .