Identifying key actors for technology adoption in higher education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Higher education institutions are increasingly implementing strategies and practices aimed towards enhancing learning for the future by integrating educational technologies with classroom instruction. Despite the notable affordances these technologies bring to the learning context, there continues to be some resistance within the academy. Senior higher education administrators or leaders are frequently challenged with developing novel strategies to influence technology adoption. Prior studies relating to technology adoption and diffusion have emphasized the importance of collaboration, mentorship, and communities of practice in influencing the level of technology acceptance. Research in social networks has also shown that key actors within a network can assist with the dissemination of information. This case study investigated the relationship between the position of instructors within their departmental social network and their level of technology adoption to begin to identify strategic access points for facilitating technology adoption within higher education.
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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 | 0.000 | 0.001 |
| 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 it