Abstract
Explainable artificial intelligence aims to describe an artificial intelligence model and its predictions. In this research work, this technique is applied to a subject of a Computer Science degree where the programming language changed from Octave to Python. Experiments are performed to analyze the explainability using the SHapley Additive exPlanations algorithm for XGBoost regressor model (for numerical grade prediction) and XGBoost classifier model (for class grade prediction). After the validation and training process, several conclusions are drawn that validate the idea of changing the programming language to a more popular one such as Python. For example, regarding classification problems, the most important feature for the insufficient class in the Octave courses is the practical exam.
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Acknowledgements
The authors would like to thank the Spanish Ministry of Science and Innovation for the support under the projects PID2020-117954RB-C21 and TED2021-131311B-C22 and the European Regional Development Fund and Junta de Andalucía for projects PY20-00870 and UPO-138516.
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Melgar-García, L., Troncoso-García, Á., Gutiérrez-Avilés, D., Torres, J.F., Troncoso, A. (2023). Explainable Artificial Intelligence for Education: A Real Case of a University Subject Switched to Python. In: García Bringas, P., et al. International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023). CISIS ICEUTE 2023 2023. Lecture Notes in Networks and Systems, vol 748. Springer, Cham. https://doi.org/10.1007/978-3-031-42519-6_34
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