Technology Acceptance among English Pre-service Teachers: A Path Analysis Approach
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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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".