Marcell Nagy Budapesti Műszaki és Gazdaságtudományi Egyetem, Matematika Intézet, Sztochasztika Tanszék Budapest Magyarország; Budapest University of Technology and Economics, Institute of Mathematics, Department of Stochastics Budapest Hungary;
eKRÉTA Informatikai Zrt. Budapest Magyarország; eKRÉTA Infromatics Budapest Hungary

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A hallgatói lemorzsolódás az egyik legégetőbb probléma a felsőoktatásban. Ebben a munkában a lemorzsolódás előrejelzésén keresztül bemutatjuk, hogyan tudják segíteni a felsőoktatás résztvevőit a magyarázható mesterséges intelligencia (XAI) eszközök, mint például a permutációs fontosság, a parciális függőségi ábra és a SHAP. Végül pedig kitérünk a kutatás gyakorlati hasznosulásának lehetőségeire, például, hogy az egyéni előrejelzések magyarázata hogyan teszi lehetővé a személyre szabott beavatkozást. Az elemzések során azt találtuk, hogy a középiskolai tanulmányi átlag bír a legnagyobb prediktív erővel a végzés tényére vonatkozóan. Továbbá annak ellenére, hogy egy műszaki egyetem adatait elemeztük, azt találtuk, hogy a humán tárgyaknak is nagy inkrementális prediktív erejük van a végzés tényére vonatkozóan a reál tárgyakhoz képest.


Delayed completion and student drop-out are some of the most critical problems in higher education, especially regarding STEM programs. A high drop-out rate induces both individual and economic loss, hence a detailed investigation of the main reasons for dropping out is warranted. Recently, there has been a lot of interest in the use of machine learning methods for the early detection of students at risk of dropping out. However, there has not been much debate on the use of interpretable machine learning (IML) and explainable artificial intelligence (XAI) technologies for dropout prediction. In this paper, we show how IML and XAI techniques can assist educational stakeholders in dropout prediction using data from the Budapest University of Technology and Economics. We demonstrate that complex black-box machine learning algorithms, for example CatBoost, are able to effectively detect at-risk student using only pre-enrollment achievement measures, but they lack interpretability. We demonstrate how the predictions can be explained both globally and locally using IML methods including permutation importance (PI), partial dependence plot (PDP), LIME, and SHAP values.

Using global interpretations, we have found that the factor that has the greatest impact on academic performance is the high school grade point average, which measures general knowledge by taking into account grades in history, mathematics, Hungarian language and literature, a foreign language and a science subject. However, we also found that both mathematics and the subject of choice are among the most important variables, which suggests that program-specific knowledge is not negligible and complements general knowledge. We discovered that students are more likely to drop out if they do not start their university studies immediately after leaving secondary school. Using a partial dependence plot, we showed that humanities also have incremental predictive power, despite the fact that this analysis is based on data from a technical university. Finally, we also discuss the potential practical applications of our work, such as how the explanation of individual predictions allows for personalized interventions, for example by offering appropriate remedial courses and tutoring sessions. Our approach is unique in that we not only estimate the probability of dropping out, but also interpret the model and provide explanations for each prediction. As a result, this framework can be used in several fields. By predicting which majors they could be most successful in based on high school performance indicators, it might, for instance, assist high school students in selecting the appropriate programs at universities and hence this way it could be used for career assistance. Through the explanations of local predictions, the framework provided can also assist students in identifying the skills they need to develop to succeed in their university studies.

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    Baranyi, M., & Molontay, R. (2021) Comparing the effectiveness of two remedial mathematics courses using modern regression discontinuity techniques. Interactive Learning Environments, Vol. 29. pp. 247–269.

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    Baranyi, M., Nagy, M., & Molontay, R. (2020) Interpretable Deep Learning for University Dropout Prediction. Proceedings of the 21st Annual Conference on Information Technology Education, pp. 13–19.

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    Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., & Murray, D. J. (2019) Identifying key factors of student academic performance by subgroup discovery. International Journal of Data Science and Analytics, Vol. 7. pp. 227–245.

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    Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019) A systematic review of deep learning approaches to educational data mining. Complexity, Vol. 2019.

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    Karimi, A.-H., Barthe, G., Balle, B., & Valera, I. (2020) Model-agnostic counterfactual explanations for consequential decisions. International Conference on Artificial Intelligence and Statistics. pp. 895–905.

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    Karlos, S., Kostopoulos, G., & Kotsiantis, S. (2020) Predicting and interpreting students’ grades in distance higher education through a semi-regression method. Applied Sciences, Vol. 10. No. 23. 8413.

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    Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. (2020) Problems with Shapley-value-based explanations as feature importance measures. International Conference on Machine Learning, Vol. 119. pp. 5491–5500.

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    Latif, A., Choudhary, A. I., & Hammayun, A. A. (2015) Economic effects of student dropouts: A comparative study. Journal of Global Economics. Vol. 3. No. 2. pp. 1–4.

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    Márquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y., Mousa Fardoun, H., & Ventura, S. (2016) Early dropout prediction using data mining: a case study with high school students. Expert Systems, Vol. 33. No. 1. pp. 107–124.

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    Mingyu, Z., Sutong, W., Yanzhang, W., & Dujuan, W. (2021) An interpretable prediction method for university student academic crisis warning. Complex & Intelligent Systems, Vol. 8. pp. 323–336.

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    Molnar, C. (2020) Interpretable Machine Learning.

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    Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C. A., … Bischl, B. (2020) General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. arXiv preprint arXiv:2007.04131.

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    Molontay, R., & Nagy, M. (2022) How to improve the predictive validity of a composite admission score? A case study from Hungary. Assessment & Evaluation in Higher Education, pp. 1–19.

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    Mothilal, R. K., Sharma, A., & Tan, C. (2020) Explaining machine learning classifiers through diverse counterfactual explanations. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 607–617.

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    Nagrecha, S., Dillon, J. Z., & Chawla, N. V. (2017) MOOC Dropout Prediction: Lessons Learned from Making Pipelines Interpretable. Proceedings of the 26th International Conference on World Wide Web Companion, pp. 351–359.

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    Nagy, M., & Molontay, R. (2021) Comprehensive analysis of the predictive validity of the university entrance score in Hungary. Assessment & Evaluation in Higher Education, Vol. 46. No. 8. pp. 1235–1253.

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    Nagy, M., Molontay, R., & Szabó, M. (2019) A web application for predicting academic performance and identifying the contributing factors. 47th Annual Conference of SEFI, pp. 1794–1806.

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    Vultureanu-Albiş I, A., & Bădică, C. (2021) Improving Students’ Performance by Interpretable Explanations using Ensemble Tree-Based Approaches. 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), pp. 215–220.

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