Deep learning with data transformation and factor analysis for student performance prediction
Open accessAbstract
Student performance prediction is one of the most concerning issues in the field of education and training, especially educational data mining. The prediction supports students to select courses and design appropriate study plans for themselves. Moreover, student performance prediction enables lecturers as well as educational managers to indicate what students should be monitored and supported to complete their programs with the best results. These supports can reduce formal warnings and expulsions from universities due to students’ poor performance. This study proposes a method to predict student performance using various deep learning techniques. Also, we analyze and present several techniques for data pre-processing (e.g., Quantile Transforms and MinMax Scaler) before fetching them into well-known deep learning models such as Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) to do prediction tasks. Experiments are built on 16 datasets related to numerous different majors with appropriately four million samples collected from the student information system of a Vietnamese multidisciplinary university. Results show that the proposed method provides good prediction results, especially when using data transformation. The results are feasible for applying to practical cases.
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Researchers have mapped the question of performance prediction into a final exam grade prediction problem.
“In a related theme, researchers have also mapped the question of performance prediction into a final exam grade prediction problem (e.g., excellent, good, fair, fail; Nghe et al., 2007; Bydžovská, 2016; Dien et al., 2020).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Dien et al., 2020
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- Authors
- Dien, T. T., Hoai, S., Thanh-Hai, N., & Thai-Nghe, N.
- Affiliations
- Can Tho University (VN)
- ORCID
- Thai-Nghe, Nguyen
- Venue
- International Journal of Advanced Computer Science and Applications
- Published
- 2020
- Volume
- 11
- Issue
- 8
- ISSN
- 2156-5570 (ISSN-L)
- 2158-107X
- OpenAlex
- W3082899649
- Cited by
- 58
- References
- 20
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 18 · 2023: 9 · 2022: 4 · 2021: 9 · 2020: 2
- FWCI
- 5.51
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Advanced Computer Science and Applications (publishedVersion, cc-by)
- International Journal of Advanced Computer Science and Applications (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Educational Outcomes and Influences
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- student performance prediction, educational data mining, data transformation, factor analysis, min-max scaling, data preprocessing, long short-term memory, convolutional neural networks, student information system
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