Preservice Teachers’ Pedagogical Mobility: A Case Study about Classroom Preparedness and Flexibility in a Disrupted Professional Placement Context
Bibliographic record
Abstract
The COVID-19 pandemic has shifted the contextual matters of education at all levels, for example, geographic location, community engagement in education and socioeconomic factors, to mention some contextual matters. Awareness of these matters stimulates critical reflections on the depth of preservice teachers’ pedagogical content and pedagogical knowledge. This paper examines preservice teachers’ pedagogical mobility in periods that rely on disruptive innovation. Preservice teachers’ placement settings changed rapidly because of COVID-19 regulations which impacted face-to-face and online teaching and learning environments. This investigation focused on professional learning under the ambit of teacher education, which up to now has been focused on face-to-face teaching pedagogies. The rapidly changing context has made the classroom the pedagogical anchor of education theory and practice. Using a reflective case study approach, we investigated (a) preservice teachers’ pedagogical challenges, (b) the meaning of pedagogical flexibility and innovative pedagogical mobility, and (c) the application of teacher performance and teaching standards in a teaching and learning environment affected by COVID-19. The critical self-reflective narratives offer insight into lived experiences and multiple contextual challenges that raise questions about well-prepared preservice teachers.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".