Educational data mining for student performance prediction in artificial intelligence environment
Abstract
In education, with the application of these information technologies, massive student data continue to produce, in order to realize the processing of data information, the traditional data mining technology is applied to the mass of education data processing process derived from a new technology, that is, education data mining technology. Among them, student performance prediction is an important application direction in education data mining, can help teachers to optimise their teaching decisions and help students to improve their learning plans. However, as of now, most of the models for student performance prediction suffer from weak generalization ability and poor feature correlation. Therefore, this paper proposes a student performance prediction method based on feature selection and Bagging integrated learning, which analyzes the model and a single prediction model, effectively solves the problem of low prediction accuracy of a single model, and improves the ability of the model to deal with the unseen examples to a certain extent, with a strong generalization ability.
References
1. Changjie Tang, Rynson W.H. Lau, Qing Li, Huabei Yin, Tong Li, Danny Kilis. Personalized courseware construction based on Web data mining[C]. First International Confe- rence on Web Information Systems Engineering, Hong Kong, China, 2000, 2, 204-211.
2. Ya-Han Hu, Chia-Lun Lo, Sheng-Pao Shih. Developing early warning systems to predict students’ online learning performance[J]. Computers in Human Behavior, 2014, 36:469-478.
3. Romero C, Lopez M I, Luna J M, et al. Predicting students’ final performance from
4. participation in on-line discussion forums[J]. Computers & Education, 2013, 68:458-472.
5. Pena-Ayala A. Educational data mining: A survey and a data mining-based analysis ofrecent works[J]. Expert systems with applications, 2014, 41(4): 1432-1462.
6. Thammasiri D, Delen D, Meesad P, et al. A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition[J]. Expert Systems with Applications, 2014, 41(2): 321-330.
7. Oyelade O J, Oladipupo O O, Obagbuwa I C. Application of k-Means Clustering algorithm for prediction of Students Academic Performance[J]. International Journal of Computer Science & Information Security, 2010, 7(1): S39.
8. Mubarak Ahmed A., Cao Han, Hezam Ibrahim M.. Deep analytic model for student dropout prediction in massive open online courses[J]. Computers and Electrical Engineering, 2021,93.
9. Ren Zhiyun, Rangwala Huzefa, Johri Aditya. Predicting Performance on MOOC
10. Assessments Using Multi-Regression Models[P]. International Educational Data Mining Society. 2016.
11. Gamulin J, Gamulin O, Kermek D. Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments[J]. Expert Systems, 2016,33.
12. Agaoglu M. Predicting instructor performance using data mining techniques in higher education[J]. IEEE Access, 2016(4): 2379-2387.
13. Tripathi A, Yadav S, Rajan R. Naive Bayes Classification Model for the Student Performance Prediction[C]. 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). 2019.
14. Tang, Xi. Analysis and Research on Students’ Image Construction and Academic Situation Prediction Based on Educational Data Mining [J]. Modern Information Technology,2023,7(04): 193-198.
15. Albreiki B, Zaki N, Alashwal H. A systematic literature review of student’performance prediction using machine learning techniques[J]. Education Sciences, 2021, 11(9): 552.
16. Xu Z, Yuan H, Liu Q. Student performance prediction based on blended learning[J]. IEEE Transactions on Education, 2020, 64(1): 66-73.
17. Hashim A S, Awadh W A, Hamoud A K. Student performance prediction model based on supervised machine learning algorithms[C]//IOP conference series: materials science and engineering. IOP Publishing, 2020, 928(3): 032019.
18. Sekeroglu B, Abiyev R, Ilhan A, et al. Systematic literature review on machine learning and student performance prediction: Critical gaps and possible remedies[J]. Applied Sciences, 2021, 11(22): 10907.
19. Chitti M, Chitti P, Jayabalan M. Need for interpretable student performance prediction[C]//2020 13th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2020: 269-272.
20. Alamri R, Alharbi B. Explainable student performance prediction models: a systematic review[J]. IEEE Access, 2021, 9: 33132-33143.
21. Bilal M, Omar M, Anwar W, et al. The role of demographic and academic features in a student performance prediction[J]. Scientific Reports, 2022, 12(1): 12508.
22. Kishor K, Sharma R, Chhabra M. Student performance prediction using technology of machine learning[C]//International Conference on Micro-Electronics and Telecommunication Engineering. Singapore: Springer Nature Singapore, 2021: 541-551.
23. Pallathadka H, Wenda A, Ramirez-Asís E, et al. Classification and prediction of student performance data using various machine learning algorithms[J]. Materials today: proceedings, 2023, 80: 3782-3785.
24. Sravani B, Bala M M. Prediction of student performance using linear regression[C]//2020 International Conference for Emerging Technology (INCET). IEEE, 2020: 1-5.
25. Xu Z, Yuan H, Liu Q. Student performance prediction based on blended learning[J]. IEEE Transactions on Education, 2020, 64(1): 66-73.
26. Yang F, Li F W B. Study on student performance estimation, student progress analysis, and student potential prediction based on data mining[J]. Computers & Education, 2018, 123: 97-108.
27. Asselman A, Khaldi M, Aammou S. Enhancing the prediction of student performance based on the machine learning XGBoost algorithm[J]. Interactive Learning Environments, 2023, 31(6): 3360-3379.
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