Digital Competence and Anxiety in Thai University English Education: Impacts on Teachers and Students
Bibliographic record
Abstract
This study explored the levels of digital competence and digital anxiety among 25 teachers and 40 students in a Thai university, examining their relationship and impact on digital identities and the quality of online English language education. Guided by Complexity Theory, a mixed-methods approach was adopted, combining quantitative questionnaires with qualitative semi-structured interviews and focus-group discussions. Results indicated significant differences in digital competence and anxiety across generational and academic groups. Younger teachers and senior students showed higher digital competence and lower digital anxiety. Additionally, a significant negative correlation between digital competence and anxiety was observed, suggesting that higher digital competence reduces anxiety. Thematic analysis further revealed that higher digital competence promotes cohesive and confident digital identities, while higher anxiety contributed to fragmented identities. These findings emphasize the importance of enhancing digital literacy and providing psychological support to improve educational outcomes. The study advocates for comprehensive digital literacy programs tailored to different generational and academic groups. Future research should involve larger, more diverse samples, consider additional variables, and explore strategies to enhance digital competence and reduce anxiety. This research offers insights into the complex interplay between digital competence, anxiety, and identity in educational contexts.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".