Abstract
E-learning can be able to act where traditional education cannot, thanks to its ease of interaction with virtual resources. In this work, the possibility of predicting the final outcome of students based solely on their interaction with virtual resources will be tested. The study aims to evaluate the effectiveness of various machine learning and deep learning models in predicting the performance of students based on their interactions with these virtual resources. The OULA dataset will be used to evaluate the proposed models to predict not only whether the student will pass or fail, but also whether the student will receive a distinction or will drop out of the course prematurely. Some of the models trained in this paper, such as Random Forest, have achieved high accuracy levels, up to 96% for binary classification and up to 80% for multiclass classification. These results indicate that it is possible to predict the performance of students based exclusively on their interactions during the duration of the course and to make predictions for each course individually. They also demonstrate the effectiveness of the proposed models and the potential of virtual resources in predicting the performance of students.
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Acknowledgement
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., Salinas, J.A., Remolar, I. (2023). Predicting Student Performance with Virtual Resources Interaction Data. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 675. Springer, Cham. https://doi.org/10.1007/978-3-031-34111-3_39
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