ICT Use in EFL Classes: A Focus on EFL Teachers’ Characteristics
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
This study investigates the level of Information and Communication Technology (ICT) use in teaching English as a foreign language (EFL). Additionally, it explores the effect of EFL teachers’ personal and technology-related characteristics in ICT use in English classes. Two hundred and forty-eight full time teachers participated in the study and filled in the personal information form, computer anxiety rating scale, computer attitude questionnaire, ICT use rating scale, and computer literacy questionnaire. The results of data analysis revealed that digital portable devices were used more than computer or network applications/tools in English classes and teachers used technology most frequently in teaching oral skills. It was also found that ICT use correlated inversely with teachers’ age, years of teaching experience, and computer anxiety. ICT use was found to be positively and significantly related to teachers’ academic credentials, computer ownership, computer literacy, and use; while ICT use was not related to attitude and gender. Multiple regressions showed that from among the variables that correlated with ICT use, teachers’ computer literacy and academic credentials could predict ICT use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| 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