The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review
Open accessAbstract
Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.
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Cites
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013) · Wiley Series in Probability and Statistics · doi:10.1002/9781118548387 · Cited by 13,861
Supervised Machine Learning: A Review of Classification Techniques
Kotsiantis, S. B. (2007) · Informatica · Cited by 4,185
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.03.012 · Cited by 3,158
Huang, S., & Fang, N. (2013) · Computers & Education · doi:10.1016/j.compedu.2012.08.015 · Cited by 373
PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES
KOTSIANTIS, S., PIERRAKEAS, C., & PINTELAS, P. (2004) · Applied Artificial Intelligence · doi:10.1080/08839510490442058 · Cited by 330
Using machine learning to predict student difficulties from learning session data
Hussain, M., Zhu, W., Zhang, W., Abidi, S. M. R., & Ali, S. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-018-9620-8 · Cited by 249
Dropout early warning systems for high school students using machine learning
Chung, J. Y., & Lee, S. (2019) · Children and Youth Services Review · doi:10.1016/j.childyouth.2018.11.030 · Cited by 211
Thammasiri, D., Delen, D., Meesad, P., & Kasap, N. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.07.046 · Cited by 199
Utilizing early engagement and machine learning to predict student outcomes
Gray, C. C., & Perkins, D. (2019) · Computers & Education · doi:10.1016/j.compedu.2018.12.006 · Cited by 199
Saqr, M., Fors, U., & Tedre, M. (2017) · Medical Teacher · doi:10.1080/0142159x.2017.1309376 · Cited by 182
Cited by
Mustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu, L., Panda, S., Kinshuk, et al. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00350-5 · Cited by 235
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
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139
Guo, S., Zheng, Y., & Zhai, X. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12491-8 · Cited by 117
Bozkurt, S. A., Aydoğan, S., Dursun Ergezen, F., & Türkoğlu, A. (2025) · International Nursing Review · doi:10.1111/inr.70018 · Cited by 63
Metadata
- Authors
- Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A.
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2020-02-04
- Volume
- 10
- Issue
- 3
- Pages
- 1042
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3005006660
- Cited by
- 367
- References
- 55
- FWCI
- 41.41
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
