The roles of generic and domain-specific mindsets in learning graphic design principles
Bibliographic record
Abstract
It is possible that individuals do not endorse a general mindset or theory of intelligence and that their mindset is specific to particular domains. There is currently a dearth of evidence to support this possibility. It is also not known how these two types of mindset influence learning behaviors and outcomes. This study investigates the roles of generic mindsets (i.e. beliefs about general ability) and domain-specific mindsets (i.e. beliefs about domain-specific abilities) in students’ learning of graphic design principles. Pre-service teachers (n = 107) played an online assessment game in which they designed three posters. For each poster, they had three chances to seek critical (i.e. constructive) feedback and one chance to revise their posters. Students’ poster performance was measured by the game, whereas their learning of graphic design principles was measured by a post-test. Results show that critical feedback-seeking moderated the relation between generic and domain-specific growth mindsets. Critical feedback-seeking improved learning outcomes only when students endorsed a weak fixed generic mindset. Theoretical implications suggest that generic and domain-specific mindsets are distinct psychological constructs, and that generic mindsets seem to be more important than domain-specific mindsets in predicting learning of graphic design principles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".