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In design courses, reviewing how others have solved design problems or completed projects is common practice and often encouraged by educators. Using student work as examples can provide context for assessment criteria and help students approach new design problems. While studies have explored the use of exemplars in various disciplines, little research has focused on which exemplars to use (e.g., high-quality, low-quality) in design, technology, and engineering fields. To address this gap, researchers conducted a literature review of 33 articles on exemplar use in secondary and post-secondary education. The analysis revealed nine themes related to exemplar use and their impact on student learning, including (1) Clarity of instruction, (2) Learner focus, (3) Motivation for learning, (4) Student reflection on learning, (5) Building student self-efficacy, (6) Identifying instructional challenges, (7) Providing contrasting cases, (8) The relationship between exemplar quality and student work quality, and (9) Raising the bar for learning outcomes. Findings suggest that simply providing an exemplar is not enough and that the selection of an exemplar can have positive or negative impacts on student motivation, understanding, and application. Carefully selecting exemplars and engaging in dialogue with students can help them identify expectations, recognize quality work, and identify potential misconceptions. These findings have implications for those involved in design, technology, and engineering education. Educators can use these findings to guide their selection of exemplars and engage students in meaningful dialogue to aid their learning. Researchers can also use these findings to further investigate the use of exemplars in these fields.
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What does a radical exemplar view not predict? A commentary on Ambridge (2020)
This article reviews two aspects of human learning: (1) people draw inferences that appear to rely on hierarchical conceptual representations; (2) some categories are much easier to learn than others given the same number of exemplars, and some categories remain difficult despite extensive training. Both of these results are difficult to reconcile with a learning and categorization system that operates only on specific exemplars. More generally, the article argues that specifying the empirical phenomena that a radical exemplar does not predict would aid in clarifying the radical exemplar proposal.
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- Award ID(s):
- 1734260
- PAR ID:
- 10547360
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- First Language
- Volume:
- 40
- Issue:
- 5-6
- ISSN:
- 0142-7237
- Format(s):
- Medium: X Size: p. 636-639
- Size(s):
- p. 636-639
- Sponsoring Org:
- National Science Foundation
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