Abstract
During the last two decades, the adoption of machine learning techniques for addressing various challenging issues in the educational domain has gained much popularity. Nevertheless, there is still a lack of research on developing AI systems that focus on the interpretability and explainability of the associated models and algorithms, thus being able to present the data analysis results in a human understandable way. In this work, we propose a new explainable framework for predicting students’ performance, which provides accurate, reliable and interpretable results. Our framework builds on the recently proposed NGBoost algorithm for the development of an efficient prediction model, as well as on the LIME and SHAP methods for providing local and global explanations, respectively. The use cases presented in this paper demonstrate the applicability of our framework and give insights about the recommendations that can be provided to educators and students.
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Notes
- 1.
It is noted that a detailed description of the features of both datasets, as well as their descriptive statistics and a complete exploratory data analysis, can be found in https://github.com/novelcore/A-new-explainable-and-interpretable-ML-based-framework-for-educational-data-mining.
- 2.
Additional information can be found in at https://github.com/novelcore/A-new-explainable-and-interpretable-ML-based-framework-for-educational-data-mining.
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Acknowledgements
This work received funding from the Horizon Europe research and innovation programme under Grant Agreement No. 101061509, project augMENTOR (Augmented Intelligence for Pedagogically Sustained Training and Education). We would like to thank the Department of Educational Sciences and Early Childhood Education, University of Patras, Greece, and the “Avgoulea-Linardatou” Microsoft Showcase School for providing us with the data used in this work.
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Livieris, I.E., Karacapilidis, N., Domalis, G., Tsakalidis, D. (2023). An Advanced Explainable and Interpretable ML-Based Framework for Educational Data Mining. In: Kubincová, Z., Caruso, F., Kim, Te., Ivanova, M., Lancia, L., Pellegrino, M.A. (eds) Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops - 13th International Conference. MIS4TEL 2023. Lecture Notes in Networks and Systems, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-031-42134-1_9
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