A supportive pedagogical package for distance learning and remote laboratories
Bibliographic record
Abstract
Laboratories are an important and integral part of the engineering curriculum and should be well integrated into a coherent learning path. In a distance learning context, the development and integration of remote laboratories represents an important technical challenge. Consequently, it is sometimes observed that the pedagogical and socio-affective aspects are neglected in the development of such laboratories. In this project, the objective has been to develop a flexible and reusable model (a full pedagogical package) designed to support the development of remote laboratory modules as well as providing an "ideal" distance learning environment that effectively supports teamwork between geographically dispersed students. The proposed model is partly inspired from the problem-based learning pedagogy with emphasis on collaboration, mutual training and peer support. Particular efforts were made to minimize the need for supervision. It is designed to make full use of the tools and flexibility provided by an existing learning management system (LMS) such as Moodle. The collaborative approach on which relies the completion of pre- and post-lab team documents is first discussed followed by a presentation of the other elements that are included in the model. The full concept is then summarized graphically, linking each module contents to their cognitive and socio-affective objectives. In addition to its usefulness as a guide to help developers design their remote laboratory interfaces, the model provides a convenient way to evaluate different aspects of a project (including teamwork and problem resolution processes) in addition to the actual learning objectives.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".