Visiting central heating plant and mechanical rooms in buildings: A case study of virtual tours to foster students’ learning in a distance course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Technical visits of installations related to course content can support learning, engagement and motivation of engineering students. However, the COVID-19 lockdown has prevented many in-person activities on campuses. In this work, we propose a framework for developing virtual tours, which we applied to replace in-person visits of heating, ventilating and air-conditioning (HVAC) rooms of buildings located on the campus in an HVAC undergraduate course. The virtual tours rely on many pictures taken in the mechanical rooms, as well as on technical drawings and information integrated in the visits. Students were provided in advance with a series of questions which they had to answer by navigating through the virtual tours. A survey allowed to assess what students appreciated and the difficulties that they encountered during the virtual visits. We found that the virtual visits had several advantages compared to in-person visits, such as allowing students to take the tour at their own pace and extending the learning experience to include other features (e.g. reading technical drawings). Different examples and suggestions of improvement are presented in the paper. The tours had a positive impact on students’ learning and engagement, with overall positive feedbacks from the students. The main hurdle encountered by students was the difficulty to locate themselves in the rooms, which we addressed by adding room layouts in the visits.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it