English Teachers’ Perceptions of Technology Integration: Are They Different From Their Peers in Engineering and Medical Science?
Bibliographic record
Abstract
The role played by subject areas in information and communication technology (ICT) integration has been insufficiently researched. This study compares English language teachers' perceptions of ICT integration with their peers in engineering and medical science in ICT integration. It also examines the effects of teachers’ sociobiographical variables (gender, age, computer proficiency, and years of teaching experience) predict teachers’ perceptions of ICT integration. A total of 180 teachers (112 males, 68 females) responded to a Teacher Technology Questionnaire (Lowther, Inan, Strahl, & Ross, 2008). Results show that among the predictor variables, computer skills had the highest relative impact on ICT integration. Furthermore, English language teachers' perceptions of ICT are reported to be similar to those of their peers in engineering and medical science. This study does not lend support to any significant role played by subject area in ICT integration. Implications for teaching are offered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".