The Integrative Model of Behavior Prediction to Explain Technology Use in Post-graduate Teacher Education Programs in the Netherlands
Why this work is in the frame
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Bibliographic record
Abstract
This study examined technology in post-graduateteacher training programs in the Netherlands. A questionnaire was completed by111 teacher educators from 12 Dutch universities with a post-graduate teachertraining. The psychological Integrative Model of Behavior Prediction ofFishbein and Azjen was applied to explain differences between teacher educatorsin the use of both hardware and software in teacher education. In addition to teachereducators’ gender, age and teaching experience, their positive attitudes towardtechnology in education were significantly related to the extent to which hardwarefacilities were used to support teacher training pedagogy. Perceived norm largelyexplained differences in the extent to which software applications were used. Soft-and hardware conditions and self-efficacy in technology did not add muchexplanatory power. Implications for technology use in post-graduate teachertraining are formulated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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