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

Author:

Zhu Ling1,Liu Guangyu2,Lv Shuang1ORCID,Chen Dongjie1,Chen Zhihong1,Li Xiang1

Affiliation:

1. School of Artificial Intelligence and Information Management, Zhejiang University of Finance and Economics, Hangzhou 310012, China

2. Key Laboratory of IoT and Information Fusion Technology, School of Automation, Hangzhou Dianzi University, Hangzhou 310005, China

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.

Funder

National Natural Science Foundation of China

“14th Five Year Plan” Teaching Reform Project of Zhejiang Province’s Tertiary Education, China

“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

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

Publisher

MDPI AG

Subject

Information Systems

Reference37 articles.

1. Research on Students’ Learning Behavior in Smart Classroom Teaching Mode;Le;China Educ. Technol. Equip.,2020

2. Analysis of Student Behavior in Data Mining Based on Decision Tree;Chen;Intell. Comput. Appl.,2020

3. Analysis and Countermeasures of Online Education Quality Problems under the Background of “Internet+”;Yalan;Comput. Knowl. Technol.,2022

4. Jointly Modeling Heterogeneous Student Behaviors and Interactions among Multiple Prediction Tasks;Liu;ACM Trans. Knowl. Discov. Data,2021

5. A correlational study of the learners online learning behavior and their academic achievements;Zhao;J. Shijiazhuang Univ. Appl.,2022

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3