Pre-Service and In-Service English as a Second Language Teachers’ Beliefs about the Use of Digital Technology in the Classroom
Bibliographic record
Abstract
It has been long accepted that teachers’ beliefs guide their classroom practices (Borg, 2006; Fang, 1996; Pajares, 1992; Woods, 1996). Yet, in the current high-tech age and with the push by mainstream education to incorporate technology in language teaching, little is known about what teachers think and feel about technology integration. Using Borg’s (2006) framework of language teacher cognition, this study investigated the beliefs of pre-service and in-service English as a Second Language (ESL) teachers (n = 35) about the use of digital technology in the classroom and the factors that influence those beliefs. The participants completed a three-part beliefs’ questionnaire and some (n = 10) were later met for one-on-one interviews. The results suggest that while the teachers value technology and its use in the ESL classroom, the two groups differed in their subscribed beliefs. These differences were traced back to the teachers’ age, classroom practice, experiences with digital technology, context(s) in which digital technology was used, and the amount of technology-related training the teachers received.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".