Virtual Site Visits: Student Perception and Preferences Towards Technology Enabled Experiential Learning
Bibliographic record
Abstract
Site visits are a key pedagogical tool within natural science and geographical education. Site visits provide an interactive experience to enable learning through the exposure to a real-world spatio-temporal environment. COVID-19 restrictions required the development of a virtual site visit for a landscape ecology course in North America. In this study, a series of digital tools were coordinated to deliver site visit information focusing on multi-sensory, multi-scalar, and multi-media information based on Kolb’s experiential learning model, particularly Step 1, the concrete experience. This research explored student’s perceptions and opinions of the digital tools provided to complete their ecological restoration management assignment and their effectiveness and usability. 4th year natural resource and environmental science students (n=52) reported predominately positive attitudes towards the use of the virtual site visit. Though students did not prefer the virtual site visit over a physical site visit, they noted that the virtual site visit digital tools did provide the same information as a site visit and that they felt they were able to understand all aspects of the physical site through the virtual site visit tools provided, particularly through the digital photographs and the 360-degree virtual reality imagery. Successful student assignments illustrated experiential learning outcomes were met.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".