Linguistic Competence and Technophobia: Digital Anxiety in the Construction of Teaching Performance
Bibliographic record
Abstract
The technophobia experienced by pre-service teachers in terms of digital malaise has increasingly emerged as a serious hindrance to their learning technology-facilitated instruction. This paper investigates the correlations among language proficiency, technophobia and teaching performance in a teacher training environment. Questionnaire responses from undergraduates in education programmed with both STAI-based data and open-ended questionnaire responses were the source of the data analyzed. The results showed that the cause of pre-service teachers’ technophobia is primarily based on situational constraints (i.e., poor access to digital infrastructure and insufficient institutional support) (50% of the responses), followed by personal factors in terms of fear of failure, low self-confidence, and unwillingness to use technology (45%). A further minority (5%) indicated that both influences played a role in shaping their opinions. The repercussions of cyberstress are obvious, such as reduced creativity, avoiding the use of technology in teaching practice, and an inclination to stick to traditional ways, leading to stagnation of interactive and innovative pedagogy. On the contrary, the research points to mitigating discourses that rely on accessible digital tools (Canva, Google Classroom, Quizizz), which provide situational affordances for larger participation, a positive self-bias empowering effect and a gradual reduction in anxiety. Through a discourse-based approach, language use is an important resource for pre-service teachers use to frame their digital anxiety experiences and reshape their teaching abilities. The study finds that addressing technophobia depends on institutional support and approaches to empowerment of student agency in the construction of digital pedagogical proficiency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".