Integrating an augmented reality sandbox challenge activity into a large-enrollment introductory geoscience lab for nonmajors produces no learning gains
Bibliographic record
Abstract
Studies have consistently documented that introductory geoscience students struggle with visualizing features presented on topographic maps. This is a problem that has the potential to increase in a digital age when engagement with maps consists primarily of GPS navigation via smartphones. Since the first augmented reality (AR) sandbox in 2012, geoscience educators have been wondering how it might be used to improve students’ ability to read topographic maps. This study examines one potential approach. Here we present the results of research that took place over a full academic year at a large, primarily undergraduate-serving, public university in 40 lab sections of introductory geology. This research assessed data from 730 comparison and experimental group participants to determine (a) students’ ability to represent a 2D topographic map in 3D, (b) the impact of the AR sandbox on student engagement, and (c) the impact of the AR sandbox on student learning. The results of this study revealed that students were successful in representing a 2D map in 3D and reported increased engagement in the experiment group due to experience with the AR sandbox; however, there was no difference in student learning between the experimental and comparison groups.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".