Designing Virtual Pathways for Exploring Glacial Landscapes of Glacier National Park, Montana, USA for Physical Geography Education
Bibliographic record
Abstract
Virtual field trips in physical geography transcend our human limitations regarding distance and accessibility, allowing students to experience exemplars of physical environments. These experiences can be critical for students to connect to the physical world beyond traditional classroom formats of communicating themes and features in physical geography. To maximize the learning potential of these experiences, designers must engage in a translational process to take resources and content from the physical world and migrate it to an online, virtual format. However, these virtual learning experiences need to account for how learners learn; and should draw heavily on the foundations of educational research and field sciences, while highlighting the awe and beauty of the natural landscape itself. Crafting these spatial stories of the natural world with learning elements requires careful and intentional design to maximize the perception of physical features, patterns, and processes at the landscape scale. To help field-trip developers comprehend the workflows used to create perceptible, rich environments that spur students’ learning, we propose a development process (TECCUPD) as a guide to navigate the intersection of education and science, using an example of geodiversity and alpine glacial landscapes found in Glacier National Park, Montana.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".