Application of Lightweight Characteristic Residual Frame in Small Sample Score Prediction
DOI:
https://doi.org/10.33736/jaspe.10465.2026Keywords:
Multi-source and Heterogeneous Data, Feature Engineering, Equal Frequency Division Box, Cascade Residual.Abstract
The analysis and modeling of educational data are of great significance to the evaluation of teaching quality and personalized learning guidance. However, the acquisition of academic data is often limited by the costs of data collection and the actual teaching scenarios that occur. Challenges like limited data access, small samples, and data sparsity make small-sample analysis both unavoidable and a persistent challenge in educational research. This study integrates the multi-feature data of 296 students majoring in computer science from a university in Zhengzhou, China. It proposed a feature residual cascade prediction framework that integrates binning technology. Firstly, the unified feature space of multimodal feature fusion is constructed through feature filtering and feature generation. Secondly, a high-precision and high-efficiency prediction model is established by combining the random forest strategy with box division residual error correction, named ReBin (Residual-Binned Model). The experimental results show that the method achieves excellent predictive performance with R ²=0.99 under limited sample conditions, and the improved ReBin model does not generate additional computational burden in terms of execution efficiency. By constructing a comprehensive comparative study of the system, significant breakthroughs have been made in both prediction accuracy and computational efficiency. This further confirms that this study not only provides an effective solution for the analysis of small sample data in education, but also provides an innovative modeling framework for the prediction research of small sample data in other fields, which has important theoretical reference and application value.
References
Li, X., Ke, S., Li, Y., Jin, W., Fu, X., Fu, G., & Bi, W. (2024). Temperature compensation based on BP neural network with small sample data for chloride ions optical fiber probe. Optics & Laser Technology, 176, 110973. https://doi.org/10.1016/j.optlastec.2024.110973
Yu, C., Li, W., Guo, Y., Sun, X., Hong, F., Sun, N., & Zhang, Q. (2024). Research on wear rate of train brake pads driven by small sample data. Wear, 536, 205169. https://doi.org/10.1016/j.wear.2023.205169
Wu, Z., Ma, C., Zhang, L., Gui, H., Liu, J., & Liu, Z. (2024). Predicting and compensating for small-sample thermal information data in precision machine tools: A spatial-temporal interactive integration network and digital twin system approach. Applied Soft Computing, 161, 111760. https://doi.org/10.1016/j.asoc.2024.111760
Li, C., Wang, L., Li, J., & Chen, Y. (2024). Application of multi-algorithm ensemble methods in high-dimensional and small-sample data of geotechnical engineering: A case study of swelling pressure of expansive soils. Journal of Rock Mechanics and Geotechnical Engineering, 16(5), 1896-1917. https://doi.org/10.1016/j.jrmge.2023.10.015
Cheng, Z., Chen, F., Zuo, E., Gong, Z., Xiao, M., Li, C., Liu, Y., Liu, P., Chen, C., Lv, X. & Chen, C. (2025). The MLSE-SCAM architecture combines with the improved DRSN-TIC model for Raman spectroscopy small-sample data learning. Expert Systems with Applications, 279, 127462. https://doi.org/10.1016/j.eswa.2025.127462
Marbouti, F., Diefes-Dux, H. A., & Madhavan, K. (2016). Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education, 103, 1-15. https://doi.org/10.1016/j.compedu.2016.09.005
Lakkaraju, H., Aguiar, E., Shan, C., Miller, D., Bhanpuri, N., Ghani, R., & Addison, K. L. (2015, August). A machine learning framework to identify students at risk of adverse academic outcomes. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, Sydney, NSW, Australia (pp. 1909-1918). https://doi.org/10.1145/2783258.2788620
Zeineddine, H., Braendle, U., & Farah, A. (2021). Enhancing prediction of student success: Automated machine learning approach. Computers & Electrical Engineering, 89, 106903. https://doi.org/10.1016/j.compeleceng.2020.106903
Aggarwal, D., Mittal, S., & Bali, V. (2021). Significance of non-academic parameters for predicting student performance using ensemble learning techniques. International Journal of System Dynamics Applications (IJSDA), 10(3), 38-49. DOI: 10.4018/IJSDA.2021070103
Bache K. & Silva A, M. G. (2013).The development of a student performance prediction model for improved student retention. In Proceedings of the 5th Annual Future Business Technology Conference.
Kovacic Z. (2010).Early prediction of student success: Mining students’ enrolment data. In Proceedings of the InSITE 2010: Informing Science , IT Education Conference, Cassino, Italy.
Hu, Y. H., Lo, C. L., & Shih, S. P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 36, 469-478. https://doi.org/10.1016/j.chb.2014.04.002
Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., Schmidt-Erfurth, U. & Langs, G. (2017). Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[C]. International Conference on Information Processing in Medical Imaging.Cham:Springer, 145-157.https://doi.org/10.48550/arXiv.1703.05921
Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., Vaclavek, J., & Wolff, A. (2015). OU Analyse: analysing at-risk students at The Open University. Learning analytics review, 1-16. http://www.laceproject.eu/learning-analyticsreview/analysing-at-risk-students-at-open-university/
Xu, J., Moon, K. H., & Van Der Schaar, M. (2017). A machine learning approach for tracking and predicting student performance in degree programs. IEEE Journal of Selected Topics in Signal Processing, 11(5), 742-753. https://doi.org/10.1109/JSTSP.2017.2692560
Watson, C., Li, F. W., & Godwin, J. L. (2013, July). Predicting performance in an introductory programming course by logging and analyzing student programming behavior. In 2013 IEEE 13th international conference on advanced learning technologies (pp. 319-323). IEEE. https://doi.org/10.1109/ICALT.2013.99
Al-Shehri, H., Al-Qarni, A., Al-Saati, L., Batoaq, A., Badukhen, H., Alrashed, S., Alhiyafi, J. & Olatunji, S. O. (2017, April). Student performance prediction using support vector machine and k-nearest neighbor. In 2017 IEEE 30th canadian conference on electrical and computer engineering (CCECE) (pp. 1-4). IEEE. doi: 10.1109/CCECE.2017.7946847.
Buenaño-Fernández, D., Gil, D., & Luján-Mora, S. (2019). Application of machine learning in predicting performance for computer engineering students: A case study. Sustainability, 11(10), 2833. https://doi.org/10.3390/su11102833
Niyogisubizo, J., Liao, L., Nziyumva, E., Murwanashyaka, E., & Nshimyumukiza, P. C. (2022). Predicting student's dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization. Computers and Education: Artificial Intelligence, 3, 100066. https://doi.org/10.1016/j.caeai.2022.100066
Thaker K.,Huang Y., Brusilovsky P., Daqing H. (2018).Dynamic knowledge modeling with heterogeneous activities for adaptive textbooks. In Proceedings of the 11th International Conference on Educational Data Mining, Buffalo, NY, USA, 592–595.
He, J., Bailey, J., Rubinstein, B., & Zhang, R. (2015). Identifying At-Risk Students in Massive Open Online Courses. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1), 25-30. https://doi.org/10.1609/aaai.v29i1.9471
Iqbal, Z., Qadir, J., Mian, A. N., & Kamiran, F. (2017). Machine learning based student grade prediction: A case study. arXiv preprint arXiv:1708.08744. https://doi.org/10.48550/arXiv.1708.08744
Chowdhury, S., Mayilvahanan, P., & Govindaraj, R. (2022). Optimal feature extraction and classification-oriented medical insurance prediction model: machine learning integrated with the internet of things. International Journal of Computers and Applications, 44(3), 278-290. https://doi.org/10.1080/1206212X.2020.1733307
Daud, A., Aljohani, N. R., Abbasi, R. A., Lytras, M. D., Abbas, F., & Alowibdi, J. S. (2017, April). Predicting student performance using advanced learning analytics. In Proceedings of the 26th international conference on world wide web companion (pp. 415-421). https://doi.org/10.1145/3041021.3054164
Kotsiantis, S., Patriarcheas, K., & Xenos, M. (2010). A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education. Knowledge-Based Systems, 23(6), 529-535. https://doi.org/10.1016/j.knosys.2010.03.010
Sghir, N., Adadi, A., & Lahmer, M. (2023). Recent advances in predictive learning analytics: A decade systematic review (2012–2022). Education and information technologies, 28(7), 8299-8333. https://doi.org/10.1007/s10639-022-11536-0
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 UNIMAS Publisher
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and reference publication in the Journal.
2) For open access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the CC-BY-NC-SA (Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds copyright, or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subject to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) is/are responsible to ensure his or her or their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. Corresponding author(s) has (have) obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If submitted manuscript includes materials from others, the authors have obtained the permission from the copyright owners.
5) In signing this statement, the author(s) declare(s) that the researches in which they have conducted are in compliance with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving human or the use of animal samples must obtain approval from Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that he or she has no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor or UNIMAS Publisher.
To download Copyright Transfer Statement for Journal, click here
