Validating an Instrument to Measure Teachers’Preparedness to Use Digital Technology in their Teaching
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
In order to effectively integrate digital technology into education, it is necessary to examine and understand teachers’ preparedness to use digital technology in education. The objective of this pilot study is to validate a self-reported instrument to measure teachers’ preparedness to use Information and Communication Technologies for learning and teaching. The survey items of the instrument are grounded and developed on the basis of the Unified Theory of Acceptance and Use of Technology and Technological Pedagogical Content Knowledge. Data was collected from a sample of 157 teachers at seven K-9 schools in Sweden and analysed mainly using exploratory factor analysis. The results yielded a seven-factor structure comprising a model of teachers’ digital competence focusing on their preparedness. These factors are: (1) Abilities to use digital learning technology, (2) Social influence and support, (3) Intention of use, (4) Usefulness and efficiency, (5) Limitation awareness, (6) Pedagogical potential, and (7) Assistance awareness. The results of this study aim to support schools when encouraging and supporting teachers to use technology in teaching and learning. They can also be used to measure differences before and after inventions, such as on the job teacher training.
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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 | 0.002 |
| 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 | 0.005 | 0.022 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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