The Processes of Designing and Implementing Globally Networked Learning Environments and their Implications on College Instructors’ Professional Learning: The Case of Québec CÉGEPs
Bibliographic record
Abstract
Abstract This study describes the design and implementation processes of globally networked learning environments (GNLEs) in a college environment and discusses how these processes may contribute to instructors' professional learning. A thematic analysis was conducted on five interviews with instructors working in Quebec general and vocational colleges (CEGEPs). The design and implementation processes were mapped out using Fretchling's (2007) logic modeling basic components. Findings suggest that GNLEs in a college context take the form of joint lectures or joint activities. Instructors reported that designing and teaching within a GNLE had led to pedagogical, intercultural and technology-related learning; and that learning was fostered by unforeseen challenges as well as long-standing partnerships. Résumé L’objectif de cette étude est de décrire les processus de conception et de mise en œuvre de milieux d’apprentissage réseautés internationalement (MARI) dans un environnement collégial ainsi que d’analyser leur influence sur le perfectionnement professionnel. Une analyse thématique réalisée à partir de cinq entrevues avec des enseignants de cégeps a permis d’élaborer un modèle logique (Fretchling 2007). Les résultats suggèrent que les MARI implantés dans un collège prennent surtout la forme de cours magistraux ou d’activités pédagogiques conjointes. Pour les enseignants impliqués, le perfectionnement (pédagogique, interculturel ou technologique) résulte des imprévus et des partenariats durables inhérents aux MARI.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".