The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
A Systematic Review on Educational Data Mining
Open accessAbstract
Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983-2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.
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Cites
(1983) · Cited by 33,192
Algorithms for Clustering Data
Sarle, W. S. (1990) · Technometrics · doi:10.1080/00401706.1990.10484648 · Cited by 7,865
Algorithms for clustering data
Flury, B. (1989) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(89)90033-5 · Cited by 6,432
Survey of Clustering Algorithms
Xu, R., & WunschII, D. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.845141 · Cited by 6,197
(2011) · Cited by 5,203
Systematic Literature Reviews in Software Engineering—A Systematic Literature Review
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.009 · Cited by 4,537
A Survey of Clustering Data Mining Techniques
Berkhin, P. (2006) · Grouping Multidimensional Data · doi:10.1007/3-540-28349-8_2 · Cited by 2,804
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Huang, Z. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009769707641 · Cited by 2,505
Educational Data Mining: A review of the state of the art
Romero, C., & Ventura, S. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2053532 · Cited by 2,251
Educational data mining: A survey from 1995 to 2005
Romero, C., & Ventura, S. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.04.005 · Cited by 1,482
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AI-based personalized E-learning systems: Issues, challenges, and solutions
Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022) · IEEE Access · doi:10.1109/access.2022.3193938 · Cited by 386
Predicting academic performance: a systematic literature review
Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N. (2018) · Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education · doi:10.1145/3293881.3295783 · Cited by 355
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
Charitopoulos, A., Rangoussi, M., & Koulouriotis, D. (2020) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00200-8 · Cited by 89
Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education
Alvarado-Uribe, J., Mejía-Almada, P., Masetto Herrera, A. L., Molontay, R., Hilliger, I., Hegde, V., Montemayor Gallegos, J. E., Ramírez Díaz, R. A., & Ceballos, H. G. (2022) · Data · doi:10.3390/data7090119 · Cited by 55
Metadata
- Authors
- Dutt, A., Ismail, M. A., & Herawan, T.
- Venue
- IEEE Access
- Published
- 2017
- Volume
- 5
- Pages
- 15991-16005
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2572386335
- Cited by
- 528
- References
- 101
- FWCI
- 14.76
- Type
- journal article
- Language
- en
- Open access
- gold
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Data sources
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.
