Skip to main content

An Advanced Explainable and Interpretable ML-Based Framework for Educational Data Mining

  • Conference paper
  • First Online:
Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops - 13th International Conference (MIS4TEL 2023)

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 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. 2.

    Additional information can be found in at https://github.com/novelcore/A-new-explainable-and-interpretable-ML-based-framework-for-educational-data-mining.

References

  1. Altmann, A., Toloşi, L., Sander, O., Lengauer, T.: Permutation importance: a corrected feature importance measure. Bioinformatics 26(10), 1340–1347 (2010)

    Article  Google Scholar 

  2. Carvalho, D.V., Pereira, E.M., Cardoso, J.S.: Machine learning interpretability: a survey on methods and metrics. Electronics 8(8), 832 (2019)

    Article  Google Scholar 

  3. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)

    Google Scholar 

  4. Duan, T., et al.: NGBoost: natural gradient boosting for probabilistic prediction. In: International Conference on Machine Learning, pp. 2690–2700. PMLR (2020)

    Google Scholar 

  5. Filippidi, A., Tselios, N., Komis, V.: Impact of Moodle usage practices on students’ performance in the context of a blended learning environment. In: Proceedings of Social Applications for Life Long Learning, pp. 2–7 (2010)

    Google Scholar 

  6. Grinsztajn, L., Oyallon, E., Varoquaux, G.: Why do tree-based models still outperform deep learning on tabular data? arXiv preprint arXiv:2207.08815 (2022)

  7. Guleria, P., Sood, M.: Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling. Educ. Inf. Technol. 28(1), 1081–1116 (2023)

    Article  Google Scholar 

  8. Hur, P., Lee, H., Bhat, S., Bosch, N.: Using machine learning explainability methods to personalize interventions for students. International Educational Data Mining Society (2022)

    Google Scholar 

  9. Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30 (2017)

    Google Scholar 

  10. Knapič, S., Malhi, A., Saluja, R., Främling, K.: Explainable artificial intelligence for human decision support system in the medical domain. Mach. Learn. Knowl. Extract. 3(3), 740–770 (2021)

    Article  Google Scholar 

  11. Liaw, A., Wiener, M., et al.: Classification and regression by Random-Forest. R News 2(3), 18–22 (2002)

    Google Scholar 

  12. Livieris, I.E., Drakopoulou, K., Tampakas, V.T., Mikropoulos, T.A., Pintelas, P.: Predicting secondary school students’ performance utilizing a semi-supervised learning approach. J. Educ. Comput. Res. 57(2), 448–470 (2019)

    Article  Google Scholar 

  13. Livieris, I.E., Kiriakidou, N., Stavroyiannis, S., Pintelas, P.: An advanced CNN-LSTM model for cryptocurrency forecasting. Electronics 10(3), 287 (2021)

    Article  Google Scholar 

  14. Livieris, I.E., Kotsilieris, T., Tampakas, V., Pintelas, P.: Improving the evaluation process of students’ performance utilizing a decision support software. Neural Comput. Appl. 31, 1683–1694 (2019)

    Article  Google Scholar 

  15. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30 (2017)

    Google Scholar 

  16. Ramaswami, G., Susnjak, T., Mathrani, A.: On developing generic models for predicting student outcomes in educational data mining. Big Data Cogn. Comput. 6(1), 6 (2022)

    Article  Google Scholar 

  17. Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)

    Google Scholar 

  18. Tampakas, V., Livieris, I.E., Pintelas, E., Karacapilidis, N., Pintelas, P.: Prediction of students’ graduation time using a two-level classification algorithm. In: Tsitouridou, M.A., Diniz, J., Mikropoulos, T.A. (eds.) TECH-EDU 2018. CCIS, vol. 993, pp. 553–565. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20954-4_42

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ioannis E. Livieris .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

Publish with us

Policies and ethics