Understanding self-directed learning behaviors in a computer-aided 3D design context

Self-directed learning (SDL) has been employed in design education to support students' effective learning and designing. However, it is still unknown how students engage in an SDL process and how students’ SDL behaviors affect performance in a design context. In a computer-aided 3D design context, this study used cluster analysis to identify SDL behavioral patterns based on the trace data of 193 middle school students and further examined the differences in the perceived SDL ability and creative performance. Four distinct SDL behavioral patterns were identified: fully engaged, planning and reflection engaged, execution and regulation engaged, and minimally engaged learners. There were significant differences in the perceived SDL ability and creative performance between the four SDL behavioral patterns. The fully engaged learners showed the highest levels of perceived SDL ability and creative performance; the minimally engaged learners showed the lowest levels of perceived SDL ability and creative performance; the planning and reflection engaged learners had higher levels of perceived SDL ability and creative performance than the execution and regulation engaged learners. The findings provide insights for better understanding SDL from a behavioral perspective and for effective incorporation of SDL in a design context.