STEM Teacher Candidates’ Preparation for Online Teaching: Promoting Technological and Pedagogical Knowledge
Bibliographic record
Abstract
Emergency remote teaching during the COVID-19 pandemic has shed light on pedagogical challenges that require the immediate attention of teacher education programs. This paper focuses on teacher candidates’ preparation to teach online in a STEM curriculum and pedagogy course in a teacher education program at a Canadian university. The authors present a two-phase study of two cohorts of teacher candidates enrolled in this course and explore 1) their perceptions of the dynamics and effectiveness of online teaching as a teaching modality, and 2) the impact of the course on their technological and pedagogical skills necessary for online teaching. Quantitative and qualitative data were collected through pre- and post-surveys administered online at the beginning and end of the course. Findings suggest that teacher candidates’ engagement with course content resulted in a notable improvement in their views toward online teaching as a teaching modality, pedagogical approaches, and personal abilities utilizing innovative online teaching strategies. This research emphasizes the necessity for comprehensive training programs that enhance teacher candidates’ technological competencies while simultaneously refining their pedagogical methodologies for online settings. Implications for teacher education research and practice are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".