Virtual field experiences in introductory geology: Addressing a capacity problem, but finding a pedagogical one
Bibliographic record
Abstract
Recent literature has demonstrated the importance of fieldwork in geology. However, as resources become scarce, field experiences are often targeted for cuts. This was the case at the University of Calgary when massive enrollments placed a tremendous burden on resources. In courses throughout, field trips and other excursions were eliminated, making it so students do not have any field experiences until their third year. In response, we have developed three virtual field experiences (VFEs) of geologically relevant locations near Calgary. A burgeoning technology, VFEs offer advantages of convenience and versatility when compared to actual field trips. Our VFEs comprise drone-captured images used to form high-resolution 2-D photomosaics and 3-D computer models. We piloted one VFE in an introductory geology course. We wanted to understand how students engaged with the models so that we could make the VFE as effective as possible. Observing student engagement over two iterations allowed us to make changes to the activity. We found that students had difficulties with the VFE’s open endedness. They also demonstrated difficulty with the relationship between observations and inferences. This is indicative of a broader issue with how geology (or science in general) is taught. Traditional instruction in geology places great emphasis on the “what” of geology as opposed to the “how.” We contend that teaching geology with more emphasis on how geology works will help students develop a better understanding of the relationship between inference and observation, enhancing their fieldwork and their understanding of science.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".