Effects of specificity of self-explanation prompts for worked examples on computer-supported learning of collaborative diagnostic reasoning

Collaborative diagnostic reasoning (CDR) is a critical yet cognitively demanding skill in many professional domains and an example for problems that require collaboration for its solution. This study explores how novice diagnosticians—specifically automotive technician trainees—can effectively learn complex collaborative skills such as CDR through computer-supported instruction. Drawing on research on collaborative diagnostic reasoning, example-based learning and cognitive load, we compared two instructional approaches: learning by self-explaining worked examples and learning by problem-solving. Furthermore, we examined how the specificity of self-explanation prompts (specific versus general) of worked examples interacted with learners’ prior CDR skills. In a prepost experiment, 154 trainees (77 dyads) were assigned to one of three learning conditions: worked examples with specific prompts, worked examples with general prompts, and solving problems without worked examples. Knowledge of CDR strategies, quality of the CDR process and outcome, and cognitive load were measured. Our results demonstrated that self-explaining worked examples significantly improved declarative knowledge of CDR strategies and the quality of the process compared with solving problems. However, worked examples did not improve the application of CDR strategy knowledge or reduce cognitive load. Contrary to expectations, problem-solving resulted in a higher quality of the CDR outcome than self-explaining worked examples. The specificity of the prompts demonstrated no significant effects. Overall, our findings suggest that self-explaining worked examples support early stages of learning complex collaboration skills such as CDR, even in short-term interventions, while more supported practice in problem-solving seems necessary for the development of improved procedural skills.