Technologies: Contrasting Earlier Adopters
Bibliographic record
Abstract
ABSTRACT..! J The adoption of teaching and learning technologies is an innovation that challenges the structure, culture and practice of modern research universities. This paper documents quantitatively and qualitatively the attitudes, skills and behavior of the faculty related to the u~e of instruc-tional technology at a large Canadian research university. The data was gathered from a survey (n = 557) of teaching faculty. The data is ana-lyzed with respect to Roger's (1995) categories of adoption of innova-tion differentiating "Earlier Adopters " {EAs} from "Mainstream Faculty" (MF). The paper discusses four factors that have tended to create a "chasm " between these two groups and discusses strategies for reducing the chasm and providing support and incentive for all faculty in the adoption of instructional technologies.-RESUME L ' adoption de la technologie educative a des fins pedagogiques constitue une innovation qui remet en question la structure, la culture et les pratiques des universites de recherche modemes. Cet article presente des donnees qualitatives et quantitatives sur les attitudes, les habitudes et
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".