Technology Acceptance among English Pre-service Teachers: A Path Analysis Approach
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
Incorporating technology in English language teaching practices has the potential to generate more lively and captivating learning experiences for learners. The challenge lies in adequately equipping pre-service English language teachers with the skills to seamlessly incorporate technology in their teaching methods and improve students' academic performance, despite their favorable perception of its usefulness. The study aimed to shed light on the factors that contribute to pre-service teachers' acceptance of technology and to determine the applicability of the Technology Acceptance Model in the context of English language Teaching (ELT). For this study, the framework developed by Teo (2009) was utilized. The participants were 286 English pre-service teachers. The study identified 21 pairs of factors that positively and significantly affect technology acceptance, with Perceived Usefulness having the highest correlation coefficient and Facilitating Conditions having the lowest.   The path analysis of the technology acceptance model revealed that while the model was a good fit for this study, there were two non-significant paths. Perceived Ease of Use and Perceived Usefulness were found to directly affect technology acceptance, while Technological Complexity and Facilitating Conditions had indirect effects through Perceived Ease of Use and Perceived Usefulness. The results of this study showed that interventions to improve technology acceptance amongst pre-service teachers should take into account direct and indirect factors.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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