Abstract
E-learning has advantages over traditional education thanks to its flexibility and scope. This study aims to test whether it is possible to predict students’ final outcomes solely based on their interaction with virtual resources at different stages of the course. Early prediction of the outcome of students at the first stages of the course is an advantage, allowing teachers to react quickly and provide the necessary support for the students who are in need of help. The effectiveness of different machine learning and deep learning models in predicting student performance throughout the stages has been evaluated using the OULA dataset. The models will predict whether a student will pass or fail, earn a distinction, or drop out of the course prematurely. The study shows that it is possible to predict student performance based on their interactions with virtual resources during each stage of the course.
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Acknowledgment
Research supported by the e-DIPLOMA, project number 101061424, funded by the European Union. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.
Research is also supported by the Valencian Graduate School and Research Network of Artificial Intelligence (valgrAI).
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Martínez-Martínez, A., Montoliu, R., Remolar, I. (2023). Predicting Student Performance with Virtual Resources Interaction Data at Different Stages of the Course. In: Milrad, M., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning, 13th International Conference. MIS4TEL 2023. Lecture Notes in Networks and Systems, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-031-41226-4_23
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