The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques
Open accessAbstract
The main objective of this study is to apply data mining techniques to predict and analyze students' academic performance based on their academic record and forum participation.Educational Data Mining (EDM) is an emerging tool for academic intervention.The educational institutions can use EDU for extensive analysis of students' characteristics.In this study, we have collected students' data from two undergraduate courses.Three different data mining classification algorithms (Naï ve Bayes, Neural Network, and Decision Tree) were used on the dataset.The prediction performance of three classifiers are measured and compared.It was observed that Naï ve Bayes classifier outperforms other two classifiers by achieving overall prediction accuracy of 86%.This study will help teachers to improve student academic performance.
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Cites
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
Cited by
Predicting Academic Performance of Students Using a Hybrid Data Mining Approach
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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
Rajendran, S., Chamundeswari, S., & Sinha, A. A. (2022) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2022.100357 · Cited by 56
Recent trends in computational intelligence for educational big data analysis
Ikegwu, A. C., Nweke, H. F., & Anikwe, C. V. (2024) · Iran Journal of Computer Science · doi:10.1007/s42044-023-00158-5 · Cited by 53
Application of Machine Learning on Student Data for the Appraisal of Academic Performance
Alloghani, M., Al-Jumeily, D., Hussain, A., Aljaaf, A. J., Mustafina, J., & Petrov, E. (2018) · 2018 11th International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese.2018.00038 · Cited by 14
Metadata
- Authors
- King Abdulaziz University, S. A., Mueen, A., Zafar, B., & Manzoor, U.
- Venue
- International Journal of Modern Education and Computer Science
- Publisher
- MECS Publisher
- Published
- 2016-11-08
- Volume
- 8
- Issue
- 11
- Pages
- 36-42
- ISSN
- 2075-0161 (ISSN-L)
- 2075-017X
- OpenAlex
- W2558171996
- Cited by
- 212
- References
- 2
- FWCI
- 22.79
- Type
- journal article
- Language
- en
- Open access
- diamond
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- King Abdulaziz University, S. A., Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques. International Journal of Modern Education and Computer Science. https://doi.org/10.5815/ijmecs.2016.11.05
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.
