Ecosystem Education with Augmented Reality: A Flexible Tool for In-Field Learning
Bibliographic record
Abstract
Field-based learning is central to education in the biogeosciences, but COVID-19 and perennial challenges of large classes, short class times, and crowded schedules make exploration of alternative tools for field education more urgent than ever. Augmented reality (AR) is one candidate, allowing students to visit sites on their own time using a mobile app that guides them through a field trip via geolocationally triggered audio, images, and other media. Research into AR’s pedagogical effectiveness is in its infancy, seldom directly comparing delivery of the same field learning activity via AR versus in person, or investigating AR’s pedagogical value beyond the cognitive domain. To address this gap, we developed an AR version of an existing forest ecology field trip to a Douglas fir forest remnant in Vancouver, British Columbia, in a large undergraduate biogeoscience course, and compared student experiences of both versions. The study showed that AR can overcome obstacles to effective field education in large courses and deliver significant pedagogical benefits compared to conventional field trips, including engagement, enjoyment, flexibility, accessibility, and learning supports. With effective instructions and technological contingency planning, AR can be an effective tool for geoscientific field education and help address some larger pedagogical issues facing higher education.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".