Scaffolding Third Graders’ Computational Thinking in an Artificial Intelligence Course: Concepts, Practice, and Perspectives
Yu‐Ru Lin, Yuqin Yang, Yi Zhang, Daner Sun · Proceedings. · 2025
The effect of scaffolding type and timing on computational thinking (CT) for elementary school students in artificial intelligence (AI) learning is unknown.To address this research gap, a 2×2 factorial experiment was conducted in this study to examine the effect of scaffolding type (conceptual versus problem-solving) and timing (immediate versus delayed) on CT concepts, practices, and perspectives.A total of 136 third graders from four classes were assigned to four experimental conditions.The results, as measured by knowledge tests, showed that problem-solving scaffolding and immediate scaffolding were more effective in developing students' CT concepts.Lag sequential analysis indicated that students guided by problemsolving scaffolding and delayed scaffolding developed more complete problem-solving paths and achieved higher levels of CT practice.Additionally, an analysis of students' learning reflections revealed that most students could explain the functions of machine learning techniques.By highlighting effective scaffolding methods, this study advances CT education.