Engaging Student Teachers in An Online Teaching Pedagogies Module during COVID-19
Bibliographic record
Abstract
COVID 19 represented a major challenge for many educators, with teachers needing to pivot rapidly to using online learning tools in order to stay connected with their students. This was particularly relevant for teachers in the process of completing their Initial Teacher Education (ITE), whose programmes of study did not include online teaching components. The objective of this study was to develop and evaluate a module for teaching in online and blended learning environments for 244 post-primary teachers in ITE. This study begins by examining the impact of COVID-19 and the resulting pivot to online learning, this includes an overview of the challenges associated with online teaching. Next, the authors explain the module in detail, outlining the tools, strategies and activities provided for student teachers. This included peer-evaluated online micro-teaching components - which formed a major part of the learning. Data collection involved a questionnaire which gathered student teachers’ perceptions of the module and its approaches, the impact it had on their ability to teach during the COVID-19 pandemic, and the knowledge and skills useful for future practice. Findings suggested that the implementation of the developed module was successful in preparing student teachers to teach online, providing them with the tools and confidence necessary for success. Improvements suggested the development of differentiated pathways for student teachers who are more experienced with online teaching.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".