Validation of a survey instrument for understanding online teaching dexterity in higher education
Bibliographic record
Abstract
Online teaching competency has been treated as teachers’ ability to execute standardised online teaching roles. This does not capture teachers’ pedagogical agility for manoeuvring a variety of online teaching contexts. This study conceptualises online teaching dexterity as teachers’ technical dexterity with managing learning technologies, and their pedagogical agility to implement online learning through subject domain online teaching dexterity, dexterity over online modalities, online assessment dexterity, and dexterity for manifesting care to both students and self. Through an international survey of 163 higher education academics from New Zealand and China, this five-dimension model was validated with exploratory factor analysis. Subsequent cluster analysis identified four distinct online teaching dexterity profiles – All-rounders, Teaching-driven, Tech-driven, and Care-driven. These profiles reveal different teacher professional development needs through their relative confidence in the technical and pedagogical dimensions of online teaching dexterity. The relevance of Online Teaching Dexterity surveys to higher education practices 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".