The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Early dropout prediction using data mining: a case study with high school students
Abstract
Early prediction of school dropout is a serious problem in education, but it is not an easy issue to resolve. On the one hand, there are many factors that can influence student retention. On the other hand, the traditional classification approach used to solve this problem normally has to be implemented at the end of the course to gather maximum information in order to achieve the highest accuracy. In this paper, we propose a methodology and a specific classification algorithm to discover comprehensible prediction models of student dropout as soon as possible. We used data gathered from 419 high schools students in Mexico. We carried out several experiments to predict dropout at different steps of the course, to select the best indicators of dropout and to compare our proposed algorithm versus some classical and imbalanced well‐known classification algorithms. Results show that our algorithm was capable of predicting student dropout within the first 4–6 weeks of the course and trustworthy enough to be used in an early warning system.
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Cites
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Cited by
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019) · Telematics and Informatics · doi:10.1016/j.tele.2019.01.007 · Cited by 564
Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/6347186 · Cited by 382
Adnan, M., Habib, A., Ashraf, J., Mussadiq, S., Raza, A. A., Abid, M., Bashir, M., & Khan, S. U. (2021) · IEEE Access · doi:10.1109/access.2021.3049446 · Cited by 250
Abu Saa, A., Al-Emran, M., & Shaalan, K. (2019) · Technology, Knowledge and Learning · doi:10.1007/s10758-019-09408-7 · Cited by 229
Interpretable dropout prediction: towards XAI-based personalized intervention
Nagy, M., & Molontay, R. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00331-8 · Cited by 179
Metadata
- Authors
- Márquez‐Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S.
- Venue
- Expert Systems
- Publisher
- Wiley
- Published
- 2015-11-16
- Volume
- 33
- Issue
- 1
- Pages
- 107-124
- ISSN
- 0266-4720 (ISSN-L)
- 1468-0394
- OpenAlex
- W2218710975
- Cited by
- 323
- References
- 41
- FWCI
- 17.59
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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