Cited by 129

Interpretable dropout prediction: towards XAI-based personalized intervention

Nagy, Marcell, Molontay, Roland·2024·International Journal of Artificial Intelligence in Education

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s40593-023-00331-8

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 46

  1. Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A next-generation hyperpa- rameter optimization framework. Proceedings of the 25th ACM SIGKDD International Confer- ence on Knowledge Discovery and Data Mining.
  2. Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Educa- tion, 17(1), 1-21.
  3. Avella, J. T., Kebritchi, M., Nunn, S. G., & Kanai, T. (2016). Learning analytics methods, benefits, and challenges in higher education: a systematic literature review. Online Learning, 20(2), 13-29.
  4. Baranyi, M., Nagy, M., & Molontay, R. (2020). Interpretable deep learning for university dropout predic- tion. Proceedings of the 21st Annual Conference on Information Technology Education (pp. 13-19)
  5. Baranyi, M., & Molontay, R. (2021). Comparing the effectiveness of two remedial mathematics courses using modern regression discontinuity techniques. Interactive Learning Environments, 29(2), 247-269.
  6. Behr, A., Giese, M., Theune, K., et al. (2020). Early prediction of university dropouts -a random forest approach. Jahrbücher Für Nationalökonomie Und Statistik, 240(6), 743-789.
  7. Cano, A., & Leonard, J. D. (2019). Interpretable multiview early warning system adapted to underrepre- sented student populations. IEEE Transactions on Learning Technologies, 12(2), 198-211.
  8. Coussement, K., Phan, M., De Caigny, A., Benoit, D. F., & Raes, A. (2020). Predicting student dropout in subscription-based online learning environments: the beneficial impact of the logit leaf model. Decision Support Systems, 135, 113325. https:// doi. org/ 10. 1016/j. dss. 2020. 113325 · DOI · Paper page
  9. Dutt, A., Ismail, M. A., & Herawan, T. (2017). A systematic review on educational data mining. IEEE Access, 5, 15991-16005.
  10. Fisher, A., Rudin, C., & Dominici, F. (2019). All models are wrong, but many are useful: learning a variable's importance by studying an entire class of prediction models simultaneously. Journal of Machine Learning Research, 20(177), 1-81.
  11. Freitas, FAd. S., Vasconcelos, F. F., Peixoto, S. A., Hassan, M. M., Dewan, M., Albuquerque, V. HCd., et al. (2020). IoT system for school dropout prediction using machine learning techniques based on socioeconomic data. Electronics, 9(10), 1613.
  12. Greenwell, B. M., Boehmke, B. C., McCarthy, A. J. (2018). A simple and effective model-based variable importance measure. arXiv preprint arXiv:180504755.
  13. Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? In: Thirty-sixth Conference on Neural Information Processing Sys- tems Datasets and Benchmarks Track. https:// doi. org/ 10. 48550/ arXiv. 2207. 08815 · DOI
  14. He, J., Bailey, J., Rubinstein, B., & Zhang, R. (2015). Identifying at-risk students in massive open online courses. Proceedings of the AAAI Conference on Artificial Intelligence, vol 29 no 1.
  15. Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., & Murray, D. J. (2019). Identifying key factors of student academic performance by subgroup discovery. Int J Data Sci Anal, 7(3), 227-245.
  16. Jin, W., Fan, J., Gromala, D., Pasquier, P., Li, X., Hamarneh, G. (2022). Transcending XAI algorithm boundaries through end-user-inspired design. arXiv preprint arXiv:220808739.
  17. Karimi, A. H., Barthe, G., Balle, B., & Valera, I. (2020). Model-agnostic counterfactual explanations for consequential decisions. International Conference on Artificial Intelligence and Statistics, PMLR (pp. 895-905)
  18. Karlos, S., Kostopoulos, G., & Kotsiantis, S. (2020). Predicting and interpreting students' grades in dis- tance higher education through a semi-regression method. Applied Sciences, 10(23), 8413.
  19. Kumar, V. S., & Boulanger, D. (2021). Automated essay scoring and the deep learning black box: how are rubric scores determined? International Journal of Artificial Intelligence in Education, 31(3), 538-584.
  20. Latif, A., Choudhary, A. I., & Hammayun, A. A. (2015). Economic effects of student dropouts: a com- parative study. Journal of Global Economics, 3(137), 2.
  21. Lee, S., & Chung, J. Y. (2019). The machine learning-based dropout early warning system for improving the performance of dropout prediction. Applied Sciences, 9(15), 3093.
  22. Looveren, A. V., & Klaise, J. (2021). Interpretable counterfactual explanations guided by prototypes. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 650-665). Springer.
  23. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 30 (pp. 4765-4774).
  24. Márquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: a case study with high school students. Expert Systems, 33(1), 107-124.
  25. Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C. A., ... & Bischl, B. (2022, April). General pitfalls of model-agnostic interpretation methods for machine learning models. In: AI-Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020 (pp. 39-68). Cham: Springer International Publishing. https:// link. sprin ger. com/ chapt er/ 10. 1007/ 978-3-031-04083-2_4 · DOI · Paper page
  26. 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)
  27. 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)
  28. 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)
  29. 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)
  30. Nagy, M., & Molontay, R. (2021). Comprehensive analysis of the predictive validity of the university entrance score in Hungary. Assessment & Evaluation in Higher Education (pp. 1-19)
  31. Niculescu-Mizil, A., & Caruana, R. (2005). Predicting good probabilities with supervised learning. Pro- ceedings of the 22nd international conference on Machine learning (pp. 625-632)
  32. Platt, J., et al. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3), 61-74.
  33. Powell, W. W., & Snellman, K. (2004). The knowledge economy. The Annual Review of Sociology, 30, 199-220.
  34. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). Catboost: unbiased boosting with categorical features. Proceedings of the 32nd International Conference on Neural Information Processing Systems (NeurIPS'18) (pp. 1-11)
  35. Rastrollo-Guerrero, J. L., Gomez-Pulido, J. A., & Durán-Domínguez, A. (2020). Analyzing and predict- ing students' performance by means of machine learning: A review. Applied Sciences, 10(3), 1042.
  36. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016 (pp. 1135-1144)
  37. Rovira, S., Puertas, E., & Igual, L. (2017). Data-driven system to predict academic grades and dropout. PLoS One, 12(2), e0171207.
  38. Sargsyan, A., Karapetyan, A., Woon, W. L., & Alshamsi, A. (2020). Explainable AI as a Social Micro- scope: A Case Study on Academic Performance. International Conference on Machine Learning, Optimization, and Data Science (pp. 257-268). Springer.
  39. Séllei, B., Stumphauser, N., & Molontay, R. (2021). Traits versus grades-the incremental predictive power of positive psychological factors over pre-enrollment achievement measures on academic performance. Applied Sciences, 11(4), 1744.
  40. Varga, E. B., & Sátán, Á. (2021). Detecting at-risk students on computer science bachelor programs based on pre-enrollment characteristics. Hungarian Educational Research Journal, 3(11), 297-310.
  41. Vultureanu-Albişi, A., & Bădică, C. (2021). Improving students' performance by interpretable explana- tions using ensemble tree-based approaches. IEEE 15th International Symposium on Applied Com- putational Intelligence and Informatics (SACI), IEEE (pp. 215-220)
  42. Yu, H., Miao, C., Leung, C., & White, T. J. (2017). Towards AI-powered personalization in MOOC learning. npj Science of Learning, 2(1), 1-5.
  43. Yu, R., Lee, H., & Kizilcec, R. F. (2021). Should college dropout prediction models include protected attributes? Proceedings of the eighth ACM conference on learning@ scale (pp. 91-100)
  44. Zeleny, K., Molontay, R., & Szabó, M. (2021). A kollégiumi lét egyetemi teljesítményre gyakorolt hatásának vizsgálata. Statisztikai Szemle, 99(1), 46-79.
  45. Zhang, W., Zhou, Y., & Yi, B. (2019). An interpretable online learner's performance prediction model based on learning analytics. Proceedings of the 2019 11th International Conference on Education Technology and Computers (pp. 148-154)
  46. Zwick, R., & Himelfarb, I. (2011). The effect of high school socioeconomic status on the predictive valid- ity of sat scores and high school grade-point average. Journal of Educational Measurement, 48(2), 101-121.

Cited by

Sorted by citations

Authors
Nagy, M., & Molontay, R.
Venue
International Journal of Artificial Intelligence in Education
Publisher
Elsevier BV
Published
2024-06
Volume
34
Issue
2
Pages
274-300
ISSN
1560-4292
Cited by
129
References
4
Language
en
Funders
  • Nemzeti Kutatási, Fejlesztési és Innovaciós Alap (OTKA PD-142585, KDP-2020)
  • European Commission (RRF-2.3.1-21-2022-00004 Artificial Intelligence National Laboratory.)
  • Budapest University of Technology and Economics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review