How to Optimize Self-Assessment Accuracy in Cognitive Skill Acquisition When Learning from Worked Examples

Abstract

The present study was designed to understand and optimize self-assessment accuracy in cognitive skill acquisition through example-based learning. We focused on the initial problem-solving phase, which follows after studying worked examples. At the end of this phase, it is important that learners are aware whether they have already understood the solution procedure. In Experiment 1, we tested whether self-assessment accuracy depended on whether learners were prompted to infer their self-assessments from explanation-based cues (ability to explain the problems’ solutions) or from performance-based cues (problem-solving performance) and on whether learners were informed about the to-be-monitored cue before or only after the problem-solving phase. We found that performance-based cues resulted in better self-assessment accuracy and that informing learners about the to-be-monitored cue before problem-solving enhanced self-assessment accuracy. In Experiment 2, we again tested whether self-assessment accuracy depended on whether learners were prompted to infer their self-assessments from explanation- or performance-based cues. We furthermore varied whether learners received instruction on criteria for interpreting the cues and whether learners were prompted to self-explain during problem-solving. When learners received no further instructional support, like in Experiment 1, performance-based cues yielded better self-assessment accuracy. Only when learners who were prompted to infer their self-assessments from explanation-based cues received both cue criteria instruction and prompts to engage in self-explaining during problem-solving did they show similar self-assessment accuracy as learners who utilized performance-based cues. Overall, we conclude that it is more efficient to prompt learners to monitor performance-based rather than explanation-based cues in the initial problem-solving phase.