Remote Teaching of Building Information Modeling During the COVID-19 Pandemic: A Case Study
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
This article reports on a Building information modeling (BIM) distance learning experience in a pandemic context. Based on a description of the experience and a survey completed by the learners at the end of the course, the article presents and discusses various aspects of the training, including the overall satisfaction of the learners, their evaluation of the technical aspects and the practical work, as well as the proposals made to improve the course. The analysis shows that some elements of the teaching functioned well, while others were rated as being less satisfactory by the students. More specifically, the learners highlighted the need to find ways and means to improve the level of interaction, which is reduced by online education. The use of video clips as a support for practical work was recognized as being effective, but it seems useful also to resort to the use of collaborative platforms dedicated to the construction industry. A critical aspect is the remote access to computer labs with computers where the taught software is installed, as not all of the learners will always have the option of having it on their personal computers. Although the results of the experiment are difficult to generalize due to its particular context, they identify interesting avenues for improvement while paving the way to unique opportunities for the use of active pedagogy principles in BIM 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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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