Exploring the Role of Viewing Technologies in the Chemistry Classroom
Bibliographic record
Abstract
Spatial ability is an important tool in chemistry and this ability can be improved. Various technologies have been used to improve spatial ability. However, it is not clear if viewing technologies should take the place of the model kit; the traditional method of learning about molecular structures. Our research aims to address this gap. In our study, we aimed to take advantage of student affinity to technology to drive spatial ability improvements (in the context of chemistry) by having students experience molecules in virtual space using modern viewing technologies (WBVE, AR, and VR). Students were first engaged with the technologies then were assessed to see if their ability to solve problems relating to 3D-molecular structure improved. The mean spatial ability of students improved over the course of the semester (permutation test, p < 0.05) and students using model kits scored higher than those using the technologies (t-test, p < 0.05). The collection and assessment of anonymous, aggregated, student responses for this study was conducted with the approval of the University of Calgary ethics board (REB13-0724).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".