Pre-service teacher reflections, video-conference and WebCT: An exploratory case study
Bibliographic record
Abstract
Introduction. The development of video-conference and WebCT technology offers new possibilities for the education of pre-service teachers; opportunities that are only just beginning to be touched on. In this exploratory article, we investigate the opportunities for reflection that technology afforded three pre-service teachers in Canada as they a taught five elementary mathematics lessons to a remote school from their faculty of education.Method. Three pre-service teachers taught a series of mathematics lessons in a remote school via video-conference technology. Between lessons the recordings were reviewed by the pre-service teachers who subsequently engaged in WebCT discussions amongst themselves and their instructor (the third author). These commentaries were analysed in terms of the pre-service teachers‟ level of reflectivity, as either „commonsense thinkers‟ or „alert novices.‟ From this initial analysis, we then investigated three areas which the data suggests are important in assisting pre-service teachers to improve their capacities for reflection.Results. These three areas are: the biography of the pre-service teacher; the provision of content for pre-service teachers‟ reflections, and; the capacity of pre-service teachers to access the reflective opportunities afforded by the technologies.Conclusions. Based on our work, we believe that the increasing interest in utilizing video-conferencing technologies for pre-service teacher education calls for an understanding of these issues as a means for increasing the efficacy of that utilization.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".