Enhancing Predictive Performance in Identifying At-Risk Students: Integration of Topological Features, Node Embeddings in Machine Learning Models
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9379-6_10
Reference18 articles.
1. Adejo OW, Connolly T (2018) Predicting student academic performance using multi-model heterogeneous ensemble approach. J Appl Res High Educ 10(1):61–75. https://doi.org/10.1108/JARHE-09-2017-0113
2. Albreiki B, Habuza T, Shuqfa Z, Serhani MA, Zaki N, Harous S (2021) Customized rule-based model to identify at-risk students and propose rational remedial actions. Big Data Cogn Comput 5(4):71. https://doi.org/10.3390/bdcc5040071
3. Albreiki B, Habuza T, Zaki N (2022) Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rule-based models. Int J Educ Technol High Educ 19(1):49. https://doi.org/10.1186/s41239-022-00354-6
4. Albreiki B, Habuza T, Zaki N (2023) Extracting topological features to identify at-risk students using machine learning and graph convolutional network models. Int J Educ Technol High Educ 20(1):23. https://doi.org/10.1186/s41239-023-00389-3
5. Azmat G, Iriberri N (2010) The importance of relative performance feedback information: evidence from a natural experiment using high school students. J Public Econ 94(7–8):435–452. https://doi.org/10.1016/j.jpubeco.2010.04.001
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