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

Area of Science:

• Design Education
• Human-Computer Interaction
• Artificial Intelligence in Design

Background:

• Computational thinking (CT) integration in design education is underexplored.
• Digital tools and AI offer new avenues for innovation in design.
• Traditional design thinking pedagogy needs augmentation with computational methods.

Purpose of the Study:

• To investigate the impact of machine learning tools on design students' motivation and CT skills.
• To explore the use of no-code machine learning tools in product design pedagogy.
• To assess changes in learning motivation, strategies, collaboration, and critical thinking.

Main Methods:

• Experimental pedagogy involving 56 industrial design students over eight weeks.
• Utilized Waikato Environment for Knowledge Analysis (Weka), a no-code machine learning tool.
• Covariate analysis of pretest and posttest data to compare groups with and without Weka.

Main Results:

• Weka group showed significantly higher general learning motivation.
• No significant differences observed in learning strategies, collaboration, or critical thinking.
• Weka use enhanced algorithmic thinking and motivation but potentially reduced collaboration.

Conclusions:

• No-code machine learning tools can effectively boost design students' motivation for CT.
• Careful curriculum design is needed to balance computational skill development with collaborative practices.
• Integrating AI tools offers a pathway for design students to engage with emerging technologies.