Productive failure-based robot programming: Effects on primary school students’ computational thinking, self-efficacy, growth mindset, and learning perceptions

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.