The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Dropout early warning systems for high school students using machine learning
Abstract
Abstract Students' dropouts are a serious problem for students, society, and policy makers. Predictive modeling using machine learning has a great potential in developing early warning systems to identify students at risk of dropping out in advance and help them. In this study, we use the random forests in machine learning to predict students at risk of dropping out. The data used in this study are the samples of 165,715 high school students from the 2014 National Education Information System (NEIS), which is a national system for educational administration information connected through the Internet with around 12,000 elementary and secondary schools, 17 city/provincial offices of education, and the Ministry of Education in Korea. Our predictive model showed an excellent performance in predicting students' dropouts in terms of various performance metrics for binary classification. The results of our study demonstrate the benefit of using machine learning with students' big data in education. We briefly overview machine learning in general and the random forests model and present the various performance metrics to evaluate our predictive model.
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Cites
Receiver Operating Characteristic Curve in Diagnostic Test Assessment
Mandrekar, J. N. (2010) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e3181ec173d · Cited by 4,506
Finn, J. D. (1989) · Review of Educational Research · doi:10.2307/1170412 · Cited by 2,702
BALFANZ, R., HERZOG, L., & MAC IVER, D. J. (2007) · Educational Psychologist · doi:10.1080/00461520701621079 · Cited by 804
Cited by
Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review
Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A. (2020) · Applied Sciences · doi:10.3390/app10031042 · Cited by 367
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
Affordances and challenges of artificial intelligence in K-12 education: a systematic review
Crompton, H., Jones, M. V., & Burke, D. (2024) · Journal of Research on Technology in Education · doi:10.1080/15391523.2022.2121344 · Cited by 213
Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review
Zafari, M., Bazargani, J. S., Sadeghi-Niaraki, A., & Choi, S.-M. (2022) · IEEE Access · doi:10.1109/access.2022.3179356 · Cited by 185
Kearney, C. A., & Graczyk, P. A. (2020) · Clinical Child and Family Psychology Review · doi:10.1007/s10567-020-00317-1 · Cited by 157
Metadata
- Authors
- Chung, J. Y., & Lee, S.
- Venue
- Children and Youth Services Review
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 96
- Pages
- 346-353
- ISSN
- 0190-7409 (ISSN-L)
- OpenAlex
- W2901400593
- Cited by
- 211
- References
- 3
- FWCI
- 19.68
- 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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