Meeting the Challenge of Providing Flexible Learning Opportunities: Considerations for Technology Adoption amongst Academic Staff (Relever le défi de fournir des occasions d'apprentissage flexibles: considérations pour l'adoption de la technologie par le personnel universitaire).
Bibliographic record
Abstract
This paper reports on a subset of findings from a larger study investigating resistance from academic staff to the integration of technology with on-campus foreign language teaching at one North American higher education institution. The study revealed that the factors influencing technology adoption paralleled Davis’ Technology Acceptance Model’s tenets of perceived usefulness and ease of use. Further, this study supports Lai and Savage’s (2013) assertion of a lack of attention to the pedagogical affordances of technology when adoption decisions are made by instructors, thus we highlight the need for higher education leaders to determine strategies promoting awareness of the benefits technology-enabled teaching and learning can bring to advance educationally-rich flexible learning opportunities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".