Promoting the transfer of learning among school staff following an online asynchronous training on dating violence prevention: A mixed-methods study
Bibliographic record
Abstract
The SPARX team responded to the scarcity of training available to school staff concerning dating violence prevention by developing an innovative asynchronous online training program as one facet of their broader initiative. This mixed-methods study aimed to fill the existing gap in the literature regarding the factors influencing the transfer of learning, with two primary objectives. First, it sought to investigate how transfer culture and perceived social support from colleagues and school principals influenced school staff’s intention to transfer learning post-online training. Second, it aimed to uncover the barriers and facilitators associated with learning transfer six months post-training. Engaging 101 school staff members, the research utilized an online questionnaire to evaluate their intentions to transfer learning from the SPARX program’s School Staff component. Regression analysis was used to identify variables predicting intention to transfer, complemented by a qualitative multiple-case study approach focusing on the experiences of a teacher, school counsellor, and school administrator. Findings highlighted that perceived support from colleagues and opportunities to implement new skills significantly predicted a greater intention to transfer learning. In contrast, mutual support for collective learning predicted a lower intention to transfer. Key barriers included perceptions of sole responsibility for implementation and resource constraints, whereas facilitators encompassed access to human, financial, and material resources. Results underscore the pivotal role of environmental factors in promoting learning transfer within school settings, emphasizing the importance of perceived support from colleagues and advocating for a transfer culture characterized by delegated responsibilities, turnkey activities, and dedicated time for learning transfer.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".