Teaching design students machine learning to enhance motivation for learning computational thinking skills

IF 2.1 4区 心理学 Q2 PSYCHOLOGY, EXPERIMENTAL Acta Psychologica Pub Date : 2024-11-01 DOI:10.1016/j.actpsy.2024.104619
Hung-Hsiang Wang , Chun-Han Ariel Wang
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Abstract

The integration of computational thinking (CT) to enhance creativity in design students has often been underexplored in design education. While design thinking has traditionally been the cornerstone of university design pedagogy and remains essential, the increasing role of digital tools and artificial intelligence in modern design practices presents new opportunities for innovation. By introducing CT alongside design thinking, students can expand their creative toolkit and engage with emerging technologies more effectively. Although many design students may have limited experience with programming, incorporating accessible, no-code tools can help them confidently embrace computational methods, unlocking new pathways for creative exploration and innovation. This study proposes an alternative approach to improve the motivation of design students by introducing machine learning tools into product design processes. We developed an experimental pedagogy in which 56 industrial design university students learned how to use Waikato Environment for Knowledge Analysis (Weka), a machine learning tool, for three hours of design work a week, for a total of eight weeks. Our covariate analysis of data collected in the pretest and posttest shows that the general learning motivations in the group using Weka are significantly higher than those in the group without Weka. However, no significant differences were found between the two groups in terms of learning strategies, collaboration, or critical thinking. Students using Weka spent more time focusing on model training and tended to improve their algorithmic thinking, and the introduction of Weka appeared to enhance their motivation to learn. On the other hand, these students might have been focusing on working individually at their computers, potentially neglecting communication and collaboration. The findings suggest that teaching machine learning applications without requiring coding has the potential to boost design students' motivation to engage with CT skills, though care must be taken to maintain collaborative practices.

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教设计专业学生学习机器学习,增强学习计算思维技能的动机。
在设计教育中,如何整合计算思维来提高设计学生的创造力一直未得到充分的探索。虽然设计思维传统上一直是大学设计教学的基石,并且仍然是必不可少的,但数字工具和人工智能在现代设计实践中的作用越来越大,为创新提供了新的机会。通过在设计思维的基础上引入CT,学生可以扩展他们的创意工具包,更有效地参与新兴技术。虽然许多设计专业的学生在编程方面的经验可能有限,但结合可访问的、无代码的工具可以帮助他们自信地接受计算方法,为创造性的探索和创新打开新的途径。本研究提出了另一种方法,通过将机器学习工具引入产品设计过程来提高设计专业学生的动机。我们开发了一种实验教学法,让56名工业设计大学的学生学习如何使用Waikato Environment for Knowledge Analysis (Weka),这是一种机器学习工具,每周进行三小时的设计工作,总共持续八周。我们对前测和后测数据的协变量分析表明,使用Weka组的总体学习动机显著高于未使用Weka组。然而,在学习策略、合作或批判性思维方面,两组之间没有发现显著差异。使用Weka的学生将更多的时间放在模型训练上,并倾向于提高他们的算法思维,并且Weka的引入似乎增强了他们的学习动机。另一方面,这些学生可能一直专注于在电脑前单独学习,而忽视了沟通和协作。研究结果表明,在不需要编码的情况下教授机器学习应用程序有可能提高设计专业学生参与CT技能的动机,尽管必须注意保持协作实践。
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来源期刊
Acta Psychologica
Acta Psychologica PSYCHOLOGY, EXPERIMENTAL-
CiteScore
3.00
自引率
5.60%
发文量
274
审稿时长
36 weeks
期刊介绍: Acta Psychologica publishes original articles and extended reviews on selected books in any area of experimental psychology. The focus of the Journal is on empirical studies and evaluative review articles that increase the theoretical understanding of human capabilities.
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