Thinking Through the Box: Evaluating a 3D Game to Engage Penetrative Thinking
Bibliographic record
Abstract
Spatial skills allow us to mentally imagine and manipulate objects and their spatial relations. These skills are crucial in both every day and expert tasks. The present paper reports on an evaluation of a 3D game developed to train a specific spatial skill known as penetrative thinking—the ability to imagine cross-sections of 3D objects from their surface features. In the game, users change the location and orientation of a virtual plane to make cuts through 3D objects in a series of spatial puzzles. Users operate an interface to position the virtual plane until a “slice” at the location of the plane matches a target cross-section of a virtual object. Multiple spatial puzzles with different properties are completed throughout the game. In one version of the game, users completed the puzzles in an immersive virtual environment and operated a tangible interface to move the virtual plane. A secondary version of the game required users to view the puzzles in a virtual environment displayed on a computer screen, and to position the slicing plane with a keyboard and mouse. Participants (n= 45) completed a measure of penetrative thinking (Santa Barbara Solids Test) before and after completing one of three interventions: the game with the tangible interface (n= 15), the game with the keyboard interface (n= 15), or a series of (control) questions (n= 15). Although there were no significant pre-/post-intervention changes in penetrative thinking in any of the groups, participants' performance in the game correlated with scores on a standardized test of penetrative thinking. These results provide evidence that the game and the standardized test accessed similar spatial skills and, as a consequence, indicate that the 3D game has the potential to be a valid approach for training penetrative thinking skills.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".