Teaching design students machine learning to enhance motivation for learning computational thinking skills
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 artific...
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| Language: | English |
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Elsevier
2024-11-01
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| Series: | Acta Psychologica |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0001691824004979 |
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| author | Hung-Hsiang Wang Chun-Han Ariel Wang |
| author_facet | Hung-Hsiang Wang Chun-Han Ariel Wang |
| author_sort | Hung-Hsiang Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7abe2c735ab54dc2a8bcf822bda41254 |
| institution | OA Journals |
| issn | 0001-6918 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
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| series | Acta Psychologica |
| spelling | doaj-art-7abe2c735ab54dc2a8bcf822bda412542025-08-20T02:37:45ZengElsevierActa Psychologica0001-69182024-11-0125110461910.1016/j.actpsy.2024.104619Teaching design students machine learning to enhance motivation for learning computational thinking skillsHung-Hsiang Wang0Chun-Han Ariel Wang1Department of Industrial Design, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan; Corresponding author.Department of Computational Media, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USAThe 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.http://www.sciencedirect.com/science/article/pii/S0001691824004979Computational thinkingLearning motivationTeaching technologyIndustrial designMachine learningStudent self-assessment |
| spellingShingle | Hung-Hsiang Wang Chun-Han Ariel Wang Teaching design students machine learning to enhance motivation for learning computational thinking skills Acta Psychologica Computational thinking Learning motivation Teaching technology Industrial design Machine learning Student self-assessment |
| title | Teaching design students machine learning to enhance motivation for learning computational thinking skills |
| title_full | Teaching design students machine learning to enhance motivation for learning computational thinking skills |
| title_fullStr | Teaching design students machine learning to enhance motivation for learning computational thinking skills |
| title_full_unstemmed | Teaching design students machine learning to enhance motivation for learning computational thinking skills |
| title_short | Teaching design students machine learning to enhance motivation for learning computational thinking skills |
| title_sort | teaching design students machine learning to enhance motivation for learning computational thinking skills |
| topic | Computational thinking Learning motivation Teaching technology Industrial design Machine learning Student self-assessment |
| url | http://www.sciencedirect.com/science/article/pii/S0001691824004979 |
| work_keys_str_mv | AT hunghsiangwang teachingdesignstudentsmachinelearningtoenhancemotivationforlearningcomputationalthinkingskills AT chunhanarielwang teachingdesignstudentsmachinelearningtoenhancemotivationforlearningcomputationalthinkingskills |