Remote Teaching of Building Information Modeling During the COVID-19 Pandemic: A Case Study
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".