Features and strategy influence performance on a chemistry spatial task
Bibliographic record
Abstract
Spatial thinking is an important skill for success in the chemistry classroom. Experimental studies on mental rotation, a key skill for spatial thinking, show interesting trends in participant performance on 2D and 3D similarity judgement tasks. In these studies, participants’ use of mental rotation is determined by linear regression and/or correlations of response time versus angular disparity of the task. While judgements with block diagrams are amenable to mental rotation, studies find differences with the more symbolic 2D chemistry diagrams depending in the participants’ expertise: novices tend to use mental rotation, while experts use algorithms or other heuristics. Herein, we study these aspects chemistry, spatial thinking, and mental rotation, adding to prior work by investigating the effect of time limits and how task variables like axis and angular disparity influence task performance. We also incorporated a block design that allowed us to look for practice effects. This study was conducted online with undergraduate students ( N = 162) and designed as a conceptual replication of Stieff et al. (2018) to explore how features of the task (e.g., axis of rotation) influence participants accuracy, response time, and their use of mental rotation, diagrammatic, analytic, or algorithmic strategies. Our results suggest that mental rotation was being used successfully for only z-axis rotations, with other strategies such as symmetry heuristics are likely being used for x- and y-axis rotations. We also found that practice can improve stereochemistry task performance. These findings support chemistry novices’ reliance on mental rotation but also suggest that alternative strategies are used when time is limited.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".