Abstract
The use of predictive models in education promises individual support and personalization for students. To develop trustworthy models, we need to understand what factors and causes contribute to a prediction. Thus, it is necessary to develop models that are not only accurate but also explainable. Moreover, we need to conduct holistic model evaluations that also quantify explainability or other metrics next to established performance metrics. This paper explores the use of Explainable Boosting Machines (EBMs) for the task of academic risk prediction. EBMs are an extension of Generative Additive Models and promise a state-of-the-art performance on tabular datasets while being inherently interpretable. We demonstrate the benefits of using EBMs in the context of academic risk prediction trained on online learning behavior data and show the explainability of the model. Our study shows that EBMs are equally accurate as other state-of-the-art approaches while being competitive on relevant metrics for trustworthy academic risk prediction such as earliness, stability, fairness, and faithfulness of explanations. The results encourage the broader use of EBMs for other Artificial Intelligence in education tasks.
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Notes
- 1.
The code is available under https://gitlab.com/vegeedsilva/trustworthy-academic-risk-prediction-with-explainable-boosting-machine.git.
References
Adnan, M., et al.: Predicting at-risk students at different percentages of course length for early intervention using machine learning models. IEEE Access 9, 7519–7539 (2021)
Alamri, R., Alharbi, B.: Explainable student performance prediction models: a systematic review. IEEE Access 9, 33132–33143 (2021)
Baranyi, M., Nagy, M., Molontay, R.: Interpretable deep learning for university dropout prediction. In: Proceedings of the 21st Annual Conference on Information Technology Education, pp. 13–19 (2020)
Bussmann, N., Giudici, P., Marinelli, D., Papenbrock, J.: Explainable machine learning in credit risk management. Comput. Econ. 57(1), 203–216 (2021)
Chen, F., Cui, Y.: Utilizing student time series behaviour in learning management systems for early prediction of course performance. J. Learn. Anal. 7(2), 1–17 (2020)
Cohausz, L.: Towards real interpretability of student success prediction combining methods of XAI and social science. In: Proceedings of the 15th International Conference on Educational Data Mining, pp. 361–367 (2022)
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the Innovations in Theoretical CS Conference, pp. 214–226 (2012)
EU: Regulation EU 2016/679 of the European Parliament and of the Council of 27 April 2016. Official Journal of the European Union (2016)
Fiok, K., Farahani, F.V., Karwowski, W., Ahram, T.: Explainable artificial intelligence for education and training. J. Defense Model. Simul. 19(2), 133–144 (2022)
Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Adv. Neural Inf. Process. Syst. 29, 3315–3323 (2016)
Hasan, R., Fritz, M.: Understanding utility and privacy of demographic data in education technology by causal analysis and adversarial-censoring. Proc. Priv. Enhanc. Technol. 2022(2), 245–262 (2022)
Hasib, K.M., Rahman, F., Hasnat, R., Alam, M.G.R.: A machine learning and explainable AI approach for predicting secondary school student performance. In: IEEE 12th Annual Computing and Communication Workshop and Conference, pp. 0399–0405. IEEE (2022)
Hastie, T., Tibshirani, R.: Generalized additive models: some applications. J. Am. Stat. Assoc. 82(398), 371–386 (1987)
Holmes, W., et al.: Ethics of AI in education: towards a community-wide framework. Int. J. Artif. Intell. Educ. 32(3), 504–526 (2022)
Hooker, S., Erhan, D., Kindermans, P.J., Kim, B.: A benchmark for interpretability methods in deep neural networks. Adv. Neural Inf. Process. Syst. 32, 9737–9748 (2019)
Jayasundara, S., Indika, A., Herath, D.: Interpretable student performance prediction using explainable boosting machine for multi-class classification. In: 2022 2nd International Conference on Advanced Research in Computing (ICARC), pp. 391–396. IEEE (2022)
Khosravi, H., et al.: Explainable artificial intelligence in education. Comput. Educ. Artif. Intell. 3, 100074 (2022)
Kuzilek, J., Hlosta, M., Zdrahal, Z.: Open university learning analytics dataset. Sci. Data 4(1), 1–8 (2017)
Lou, Y., Caruana, R., Gehrke, J., Hooker, G.: Accurate intelligible models with pairwise interactions. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 623–631 (2013)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4765–4774 (2017)
Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1–38 (2019)
Namoun, A., Alshanqiti, A.: Predicting student performance using data mining and learning analytics techniques: a systematic literature review. Appl. Sci. 11(1), 237 (2020)
Nori, H., Caruana, R., Bu, Z., Shen, J.H., Kulkarni, J.: Accuracy, interpretability, and differential privacy via explainable boosting. In: International Conference on Machine Learning, pp. 8227–8237 (2021)
Nori, H., Jenkins, S., Koch, P., Caruana, R.: Interpretml: a unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223 (2019)
de Oliveira, C.F., Sobral, S.R., Ferreira, M.J., Moreira, F.: How does learning analytics contribute to prevent students’ dropout in higher education: a systematic literature review. Big Data Cogn. Comput. 5(4), 64 (2021)
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)
Rubiano, S.M.M., Garcia, J.A.D.: Formulation of a predictive model for academic performance based on students’ academic and demographic data. In: 2015 IEEE Frontiers in Education Conference (FIE), pp. 1–7. IEEE (2015)
Schleiss, J., Günther, K., Stober, S.: Protecting student data in ML pipelines: an overview of privacy-preserving ML. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. LNCS, vol. 13356, pp. 532–536. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-11647-6_109
Sghir, N., Adadi, A., Lahmer, M.: Recent advances in predictive learning analytics: a decade systematic review (2012–2022). Educ. Inf. Technol. 1–35 (2022)
Shin, D.: The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. Int. J. Hum. Comput. Stud. 146, 102551 (2021)
Soussia, A.B., Labba, C., Roussanaly, A., Boyer, A.: Assess performance prediction systems: Beyond precision indicators. In: Proceedings of the 14th International Conference on Computer Supported Education, pp. 489–496 (2022)
Soussia, A.B., Treuillier, C., Roussanaly, A., Boyer, A.: Learning profiles to assess educational prediction systems. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.) Artificial Intelligence in Education. AIED 2022. LNCS, vol. 13355, pp. 41–52. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-11644-5_4
Srinivasan, R., Chander, A.: Explanation perspectives from the cognitive sciences-a survey. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4812–4818 (2021)
Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning, pp. 3319–3328 (2017)
Swamy, V., Du, S., Marras, M., Kaser, T.: Trusting the explainers: teacher validation of explainable artificial intelligence for course design. In: LAK23: 13th International Learning Analytics and Knowledge Conference, pp. 345–356 (2023)
Swamy, V., Radmehr, B., Krco, N., Marras, M., Käser, T.: Evaluating the explainers: black-box explainable machine learning for student success prediction in MOOCS. In: Proceedings of the International Conference on Educational Data Mining (2022)
Vincent-Lancrin, S., van der Vlies, R.: Trustworthy artificial intelligence (AI) in education. OECD Educ. Work. Pap. 218 (2020)
Wang, C., Han, B., Patel, B., Rudin, C.: In pursuit of interpretable, fair and accurate machine learning for criminal recidivism prediction. J. Quant. Criminol. 39, 519–581 (2023)
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Dsilva, V., Schleiss, J., Stober, S. (2023). Trustworthy Academic Risk Prediction with Explainable Boosting Machines. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_38
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