How Instructors’ TPACK Developed During Emergency Remote Teaching: Evidence From Instructors in Faculties of Education
Bibliographic record
Abstract
Higher education instructors tried to find best teaching ways during the pandemic. Instructors who were faced with emergency situations used various technologies to deliver their courses. In this study, an online survey was used to ask instructors about their experiences regarding their development of technological pedagogical content knowledge (TPACK) during emergency remote teaching (ERT); 231 responses were received from instructors from faculties of education. The survey was a five-point Likert-type scale include the dimensions of pedagogical knowledge, pedagogical knowledge, technological knowledge, technological content knowledge, pedagogical content knowledge, technological pedagogical knowledge, and technological pedagogical content knowledge. Instructors rated their own non-technological knowledge (pedagogical knowledge, content knowledge, and pedagogical content knowledge) relatively higher than their knowledge including technology (technological knowledge, technological pedagogical knowledge, and technological content knowledge). The findings indicate that instructors had a consistently high level of perceived knowledge in all TPACK dimensions. Regarding developments in instructors’ TPACK, several suggestions were made, including novel technologies and pedagogies specialized for ERT.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".