Learning of abstract concepts requires generation of good quality examples One of the study strategies that teachers may suggest to students when learning abstract concepts is to generate examples. This strategy supports learning of abstract concepts by relating the abstract concepts to concrete examples. Through this process, students will comprehend the abstract concepts better and apply them to new situations. Although this strategy seems promising, Zamary, Rawson, and Dunlosky (2016) argued that students have to be good at monitoring the quality of their generated examples before they can use these examples to learn the abstract concepts. If students are poor at monitoring the quality of their generated examples, learning of the abstract concepts suffer because the poorly generated examples do not support students’ understanding of the abstract concepts.
Two experiments to examine students' accuracy in monitoring self-generated examples In view of the importance of accurately monitoring generated examples to learn abstract concepts, the authors conducted two experiments to answer their research questions:
- “How well can students evaluate the quality of their examples?"
Findings from the first experiment showed that students were overconfident when judging the quality of their examples. Participants assigned full credit more often than no credit or partial credit to incorrect and partially correct examples. The results in Experiment 1 were replicated in Experiment 2
“Is judgment accuracy influenced by the kind of feedback provided?" Participants in the experiments were given either no feedback, full definition feedback of the abstract concept, or idea unit feedback that shows segmented concepts to understand the abstract term. Results from Experiment 1 showed that the kind of feedback provided did not influence the judgment accuracy. Similarly in Experiment 2, the kind of feedback did not help students to become more accurate in evaluating the quality of their examples.
Support should be given to enhance students' accuracy in monitoring self-generated examples In line with other studies on judgment accuracy, results of Zamary et al.’s (2016) study showed that students were overconfident most of the time when judging the quality of their self-generated examples. Furthermore, the feedback provided did not increase judgement accuracy and this might be due to the possibility that students were not thinking about the quality of their examples, but were restudying the definition and thinking of their understanding of the concepts. Results from this study suggest that students’ inaccurate judgment of the quality of their examples put a restriction on self-generated examples as an effective study strategy for learning abstract concepts. Future studies should continue to examine how students can be trained or supported to become more accurate in judging the quality of their self-generated examples. Till then, students using self-generated examples to learn abstract concepts will need teachers' support in evaluating the quality of those examples.
Zamary, A., Rawson, K. A., & Dunlosky, J. (2016). How accurately can students evaluate the quality of self-generated examples of declarative concepts? Not well, and feedback does not help. Learning and Instruction, 46, 12-20.