Problem solving strategies and the relationship between visualization ability and spatial anatomy task performance
Bibliographic record
Abstract
Background In a previous study, we showed that visualization ability (VZ) is related to performance on a spatial anatomy task (SAT). Low VZ learners demonstrated consistently poorer task performance than high VZ learners. In the current study, we explored the processing commonalities and differences of learners in order to determine which problem solving strategies distinguish those of high VZ from those of low VZ. Methods Forty‐two students completed a standardize measure of VZ, the SAT, and a questionnaire involving self‐analysis of the processes and strategies used while performing the SAT. Results Consistent with our previous study, high VZ learners performed better on the SAT than low VZ learners. Concerning problem solving strategies, more low VZ learners reported using movements of body parts and/or surrounding objects while performing the SAT. These learners also stated they were more concerned about time, that is finishing all SAT questions, than they were about answering correctly. Conclusion The tendency for low VZ learners to offload cognitive work onto external perceptual‐motor processes suggests that they have problems with mental manipulations, which may contribute to their poor SAT performance. Furthermore, low VZ learners are more prone to errors while performing the SAT, as suggested by their tendency to focus on the quantity, rather than the quality, of their answers. Grant Funding Source : none
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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 | 0.001 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".