Evaluative study of a MOOC on knowledge translation in five French-speaking countries
Bibliographic record
Abstract
Over the past few years, the RENARD research team has observed a sharp increase in the need for knowledge translation (KT) training. Given the high demand, it has been nearly impossible for the team to provide training entirely in person, and so a massive open online course (MOOC) was developed. Its aim is to promote the use and appropriation of the KT process by practitioners, decision-makers, and others in the public sphere. The goal of this study was to evaluate the MOOC by collecting users' opinions, reactions, appropriation, and practice changes. A qualitative research design was used. Data were collected through semi-structured individual interviews (n = 16) with professionals from Canada, France, and three West African countries (Burkina Faso, Mali, and Senegal) who had taken the MOOC. All interviews were subjected to thematic content analysis. The MOOC content was generally appreciated and reused by the respondents. The results revealed one main motive for completing the course: the immediate opportunity to apply their learning in their practice environments. However, most respondents deplored the lack of interaction among learners and expressed the need for coaching with an instructor to deepen the topics covered during the course. The results also revealed connection and accessibility issues linked to the Internet network and unstable access to electricity in West African countries. The study highlights the potential of MOOCs for the acquisition of knowledge and competencies by KT professionals. Several recommendations and avenues of exploration were formulated to optimize and improve future designs of MOOCs on KT.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".