Bibliographic record
Abstract
Recent studies in the literature on online learning highlight a constructivist approach to knowledge-building in Web-based environments. In this case study of an online course, students were introduced to a constructivist orientation toward learning, a requirement to work in a new learning environment, and a challenge to accomplish academic work with groups of colleagues. Students learned successfully how to accommodate these requirements. In particular, this article tells how communication strategies, collaboration with one another, interaction throughout the course, and consistent participation in the growing online database supported students’ perceptions of self-efficacy and their emerging commitment to a constructivist approach to learning. Des études récentes, issues de la littérature sur l’apprentissage en ligne, mettent en relief une approche constructiviste de la construction de connaissances dans des environnements utilisant la technologie Web. Dans cette étude de cas portant sur un cours en ligne, les étudiants ont été exposés à une orientation constructiviste de l’apprentissage, ils ont dû travailler dans un nouvel environnement d’apprentissage et ils ont été mis au défi de travailler avec des groupes de collègues. Les étudiants ont appris avec succès comment s’adapter à ces conditions. L’article décrit comment les stratégies de communication, la collaboration, l’interaction continue et la participation à l’alimentation de la base de données en ligne ont contribué à donner aux étudiants un sentiment d’autoefficience et ont causé l’émergence d’un sentiment d’engagement envers une approche constructiviste de l’apprentissage.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".