Tsai Chia-Wei, Ma Yi-Wei, Chang Yao-Chung, Lai Ying-Hsun
Department of Computer Science and Information Engineering, National Taitung University, Taitung City, Taiwan.
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan.
Front Psychol. 2022 Jun 13;13:868698. doi: 10.3389/fpsyg.2022.868698. eCollection 2022.
Given the current popularization of computer programming and the trends of informatization and digitization, colleges have actively responded by making programming lessons compulsory for students of all disciplines. However, students from different ethnic groups often have different learning responses to such lessons due to their respective cultural backgrounds, the environment in which they grew up, and their consideration for future employment. In this study, an AI-assisted programming module was developed and used to compare the differences between multi-ethnic college students in terms of their theoretical and actual learning expectancy, motivation, and effectiveness. The module conducted analysis through the deep learning network and examined the relevant processes that the students underwent during programming lessons, as well as the types of errors they had committed. Their learning motivation for and actual learning performance in programming were then examined based on the cognitive learning theory. The results of the experiment, which involved 96 multi-ethnic college students, indicated that the two groups had dissimilar theoretical performance in terms of their expectancy and motivation for learning programming. The indigenous students' main concern was whether programming would affect their families or tribes, and this concern affected and was reflected in their learning outcomes. In contrast, the learning motivation and goals of Han Chinese students were driven by the cognition of the value of programming to themselves. The research findings can contribute toward the cognition and understanding of multi-ethnic students when learning computer programming and development of the appropriate teaching methods, and serve as a reference for subsequent research on integrating multiculturalism into computer programming lessons.
鉴于当前计算机编程的普及以及信息化和数字化的趋势，各高校积极响应，将编程课程设为所有学科学生的必修课。然而，由于各自的文化背景、成长环境以及对未来就业的考虑，不同民族的学生对这类课程往往有不同的学习反应。在本研究中，开发了一个人工智能辅助编程模块，并用于比较多民族大学生在理论和实际学习期望、动机及效果方面的差异。该模块通过深度学习网络进行分析，考察学生在编程课程中所经历的相关过程以及他们所犯错误的类型。然后基于认知学习理论，考察他们在编程方面的学习动机和实际学习表现。涉及96名多民族大学生的实验结果表明，两组学生在编程学习期望和动机方面的理论表现不同。本土学生主要关心的是编程是否会影响他们的家庭或部落，这种担忧影响并反映在他们的学习成果中。相比之下，汉族学生的学习动机和目标则受对编程对自身价值的认知驱动。研究结果有助于对多民族学生学习计算机编程的认知和理解，有助于开发合适的教学方法，并为后续将多元文化融入计算机编程课程的研究提供参考。
