Productive failure-based robot programming: Effects on primary school students’ computational thinking, self-efficacy, growth mindset, and learning perceptions
Yuchen Chen and Yiwei Bao · DOAJ (DOAJ: Directory of Open Access Journals) · 2026
Robot programming has become an effective means to promote primary school students’ computational thinking (CT). Direct instruction (DI), characterized by teacher instruction followed by student problem-solving, is a commonly adopted teaching approach for robot programming and CT. However, by emphasizing task completion and minimizing failures, DI limits students’ ability to understand the essence of problem-solving and learn from mistakes. In contrast, productive failure (PF) involves students’ problem-solving followed by teacher instruction, treating failure as a valuable experience that fosters deeper learning and problem-solving skills. Hence, this research compared the effects of the productive failure-based robot programming (PF-RP) and direct instruction-based robot programming (DI-RP) approaches on primary school students’ CT, self-efficacy, growth mindset, and learning perceptions. A quasi-experimental study involving both quantitative and qualitative data was designed. The sample comprised 87 third-grade students from China, with 43 students in the PF-RP group and 44 students in the DI-RP group. Results revealed that (1) both approaches enhanced students’ CT, with the PF-RP approach demonstrating greater improvements than the DI-RP approach over time; (2) both approaches improved students’ self-efficacy, but no significant differences were detected between the two approaches over time; (3) only the PF-RP approach significantly promoted students’ growth mindset and showed greater improvements than the DI-RP approach over time; and (4) students perceived that the PF-RP approach is an effective teaching approach that helped them think of more solutions and strengthen their understanding.