Applying Competition-Based Learning to Stimulate Students’ Practical and Competitive AI Ability in a Machine Learning Curriculum

IF 2.1 2区 工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES IEEE Transactions on Education Pub Date : 2024-02-23 DOI:10.1109/TE.2024.3350535
Hui-Tzu Chang;Chia-Yu Lin
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Abstract

Contribution: This study incorporates competition-based learning (CBL) into machine learning courses. By engaging students in innovative problem-solving challenges within information competitions, revealing that students’ participation in online problem-solving competitions can improve their information technology, and showcase competitions can enhance their competition ability. Background: The CBL model seamlessly integrates project-based learning and competition, placing a strong emphasis on both collective learning and outcomes. This approach cultivates motivation among team members, driving them to enhance their learning and translate knowledge into practical experience. Research Questions: The objective is to examine the disparities in the development of theoretical knowledge, information technology, AI practical ability, and competition ability among students participating in online problem-solving competitions and showcase competitions, and discusses the potential moderating effect of competition type on the relationships between variables in the hypothetical model. Methodology: The study involved 74 students enrolled in machine learning course at a university. The students were given theoretical knowledge and information technology pretests and posttests in the 2nd and 17th weeks, respectively. In the 18th week, the students presented their projects using slideshows and were graded by judges while also submitting their final competition proposal and slides. Findings: Students in online problem-solving competitions can enhance their information technology, while those participating in showcase competitions can improve their competitive ability. Moreover, the competition type was found to moderate the relationships among theoretical knowledge, information technology, and AI model accuracy. The findings suggest that incorporating CBL into machine learning courses effectively cultivates students’ AI practical and competitive abilities.
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在机器学习课程中应用竞赛式学习激发学生的人工智能实践和竞争能力
贡献:本研究将基于竞赛的学习(CBL)融入机器学习课程。通过让学生参与信息竞赛中的创新问题解决挑战,揭示了学生参与在线问题解决竞赛可以提高他们的信息技术水平,而展示竞赛可以提高他们的竞争能力。背景:基于项目的学习(CBL)模式将基于项目的学习和竞赛完美地结合在一起,强调集体学习和成果。这种方法可以培养团队成员的积极性,促使他们加强学习,将知识转化为实践经验。研究问题:目的:研究参加在线问题解决竞赛和展示竞赛的学生在理论知识、信息技术、人工智能实践能力和竞争能力发展方面的差异,并探讨竞赛类型对假设模型中变量间关系的潜在调节作用。研究方法:研究涉及某大学机器学习课程的 74 名学生。学生们分别在第 2 周和第 17 周接受了理论知识和信息技术的前测和后测。在第 18 周,学生使用幻灯片展示他们的项目,并由评委打分,同时提交他们的最终竞赛方案和幻灯片。研究结果参加在线问题解决竞赛的学生可以提高信息技术水平,而参加展示竞赛的学生可以提高竞争能力。此外,研究还发现竞赛类型对理论知识、信息技术和人工智能模型准确性之间的关系具有调节作用。研究结果表明,在机器学习课程中融入CBL能有效培养学生的人工智能实践能力和竞争能力。
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来源期刊
IEEE Transactions on Education
IEEE Transactions on Education 工程技术-工程:电子与电气
CiteScore
5.80
自引率
7.70%
发文量
90
审稿时长
1 months
期刊介绍: The IEEE Transactions on Education (ToE) publishes significant and original scholarly contributions to education in electrical and electronics engineering, computer engineering, computer science, and other fields within the scope of interest of IEEE. Contributions must address discovery, integration, and/or application of knowledge in education in these fields. Articles must support contributions and assertions with compelling evidence and provide explicit, transparent descriptions of the processes through which the evidence is collected, analyzed, and interpreted. While characteristics of compelling evidence cannot be described to address every conceivable situation, generally assessment of the work being reported must go beyond student self-report and attitudinal data.
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