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An Intelligent Boosting and Decision-Tree-Regression-Based Score Prediction (BDTR-SP) Method in the Reform of Tertiary Education Teaching

Zhu, Ling, Liu, Guangyu, Lv, Shuang, Chen, Dongjie, Chen, Zhihong, Li, Xiang·2023·Information
DOI 10.3390/info14060317
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Abstract

The reform of tertiary education teaching promotes teachers to adjust timely teaching plans based on students’ learning feedback in order to improve teaching performance. Thefore, learning score prediction is a key issue in process of the reform of tertiary education teaching. With the development of information and management technologies, a lot of teaching data are generated as the scale of online and offline education expands. However, a teacher or educator does not have a comprehensive dataset in practice, which challenges his/her ability to predict the students’ learning performance from the individual’s viewpoint. How to overcome the drawbacks of small samples is an open issue. To this end, it is desirable that an effective artificial intelligent tool is designed to help teachers or educators predict students’ scores well. We propose a boosting and decision-tree-regression-based score prediction (BDTR-SP) model, which relies on an ensemble learning structure with base learners of decision tree regression (DTR) to improve the prediction accuracy. Experiments on small samples are conducted to examine the important features that affect students’ scores. The results show that the proposed model has advantages over its peer in terms of prediction correctness. Moreover, the predicted results are consistent with the actual facts implied in the original dataset. The proposed BDTR-SP method aids teachers and students to predict students’ performance in the on-going courses in order to adjust the teaching and learning strategies, plans and practices in advance, enhancing the teaching and learning quality. Therefore, the integration of information technology and artificial intelligence into teaching and learning practices is able to push forward the reform of tertiary education teaching.

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  • Zhu et al. (2023) developed in China an AI tool to predict students' performance based on their classroom behavior and previous performance; they suggested that this tool could be used to adjust instructors' teaching strategies and improve teaching quality.

    “Using experiments with small-samples, Zhu et al. (2023) developed in China an AI tool to predict students' performance based on their classroom behavior and previous performance.They suggested that this tool could be used to adjust instructors' teaching strategies and improve teaching quality.”

    From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Zhu et al. (2023)

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Authors
Zhu, L., Liu, G., Lv, S., Chen, D., Chen, Z., & Li, X.
Affiliations
  • School of Artificial Intelligence and Information Management, Zhejiang University of Finance and Economics, Hangzhou 310012, China
  • Key Laboratory of IoT and Information Fusion Technology, School of Automation, Hangzhou Dianzi University, Hangzhou 310005, China
ORCID
Lv, Shuang
Venue
Information
Publisher
MDPI AG
Published
2023-05-30
Volume
14
Issue
6
Pages
317
ISSN
2078-2489
DOI
10.3390/info14060317
Cited by
11
References
0
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • National Natural Science Foundation of China (62002315, 62273124, jg20220384)
  • “14th Five Year Plan” Teaching Reform Project of Zhejiang Province’s Tertiary Education, China (62002315, 62273124, jg20220384)
  • “13th Five Year Plan” Virtual Simulation Experiment Teaching Project of Zhejiang Province’s Universities entitled Virtual Simulation Experiment for Programming Design in Cyber-Physical Space, China (62002315, 62273124, jg20220384)
  • Industry-University Collaborative Education Project of Zhejiang Province entitled Research on New Engineering Information Technology Talent Training Model in the Era of Artificial Intelligence, China (62002315, 62273124, jg20220384)

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