Abstract
The academic performance of a higher education student can be affected by several factors and in most cases Higher Education Institutions (HEI) have programs to intervene, prevent failure or students dropping out. These include student tutoring, mentoring, recovery classes, summer school, etc. Being able to identify the borderline cases is extremely important for planning and intervening in time. This position paper reports on an ongoing project, being developed at the University of Trás-os-Montes e Alto Douro (UTAD), which uses the students’ data and artificial intelligence algorithms to create models and predict the performance of students and classes. The main objective of the IA.EDU project is to research the usage of data, artificial intelligence and data science to create artificial intelligence solutions, including models and applications, to provide predictive information that can contribute to the increase in students’ academic success and a reduction in the dropout rate, by making it possible to act proactively with the students at risk, course directors and course designers.
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This work was supported by Project “SAMA EDU.IA”, operation number: POCI-05-5762-FSE-000199, financed by the Program COMPETE 2020, Portugal 2020.
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Reis, A., Rocha, T., Martins, P., Barroso, J. (2020). Using Artificial Intelligence to Predict Academic Performance. In: Stephanidis, C., Kurosu, M., Degen, H., Reinerman-Jones, L. (eds) HCI International 2020 - Late Breaking Papers: Multimodality and Intelligence. HCII 2020. Lecture Notes in Computer Science(), vol 12424. Springer, Cham. https://doi.org/10.1007/978-3-030-60117-1_38
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