The impact of online teaching videos on Canadian pre‐service teachers
Bibliographic record
Abstract
Purpose One of the major challenges in teacher training programs is the gap between the theory that is presented to pre‐service teachers and actual classroom practice. Many researchers, educators, and pre‐service teachers have emphasized the difficulty of linking theory and practice in teacher education programs. The purpose of this study is to better understand the impact of online teaching videos on the development of self‐efficacy beliefs in pre‐service teachers. Design/methodology/approach Over 400 student teachers participated in this study. Statistical analyses of questionnaires were conducted to assess the impact of online teaching videos. The results reveal that online videos did affect the self‐efficacy beliefs of pre‐service teachers. Findings Online videos of teaching practices appear to contribute positively to feelings of self‐efficacy in pre‐service teachers. Practical implications Given the importance of self‐efficacy for teachers' professional development, online videos could prove highly useful to this end. Apart from the impact of the videos themselves, self‐directed learning has the benefit of flexibility in terms of time and space, which is typical of open and distance learning in general. Furthermore, online videos can be readily adapted to individual professional development plans, according to the teacher's needs, in contrast to more formal training programs (either initial or continuing) with their relatively rigid, predetermined contents. Originality/value This study presents an original self‐training online video device that could easily be integrated in teacher training to support effectively their professional development.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".