Learning analytics to predict students’ performance: A case study of a neurodidactics-based collaborative learning platform

Abstract In this work, 29 features were defined and implemented to be automatically extracted and analysed in the context of NeuroK, a learning platform within the neurodidactics paradigm. Neurodidactics is an educational paradigm that addresses optimization of the learning and teaching process from the perspective of how the brain functions. In this context, the features extracted can be fed as input into various machine learning algorithms to predict the students’ performance. The proposed approach was tested with data from an international course with 698 students. Accuracies greater than 0.99 were obtained in predicting the students’ final performance. The best model was achieved with the Random Forest algorithm. It selected 7 relevant features, all with a clear interpretation in the learning process. These features are related to the principles of neurodidactics, and reflect the importance of a social learning and constructivist approach in this context. This work constitutes a first step in relating the tools of learning analytics to neurodidactics. The method, after its adaptation to capture relevant features corresponding to different contexts, could be implemented on other management learning platforms, and applied to other online courses with the aim of predicting the students’ performance, including real-time tracking of their progress and risk of dropout.