Distance Learning among Teachers and School Principals in Francophone Institutions: An Initial Knowledge Repository Associated to Individual Competencies and Organizational Capabilities for Collaborative Work
Bibliographic record
Abstract
More and more teachers and school principals in French-language minority contexts in Canada enroll in Web-based Distance Learning education programs. To reach instructional objectives, they are invited to complete collaborative learning activities using information and communication technologies (ICT). A number of studies have demonstrated the advantages of collaborative learning and practice communities for professional development. However, few studies have addressed the resources, knowledge and competencies adults need to possess to progress in a distance learning context. All the more, we know little about the knowledge management practices of educational institutions, although they offer more and more distance learning opportunities. As part of their research on organizational capabilities for the implementation of e-Government, St-Amant and Renard (2005) have elaborated a framework for the development of capabilities. According to the authors, two management fields need to be mastered in order to manage the development of organizational capabilities: problem resolution management and knowledge management. These two fields are strongly interrelated. Inspired by this work, our study aims to create and evaluate an initial knowledge repository associated with individual competencies as well as organizational capabilities for collaborative work supported by ICT in a distance learning context. This article reports on preliminary results obtained from experimentation conducted with Francophone teachers and school principals enrolled in an online distance learning program, as well as the knowledge and know-how included in an initial knowledge repository. According to the results of this study, the majority of participants found the repository useful since it has improved the quality of their work and made it easier to reach instructional objectives.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".