Examining factors contributing to technophobia: a case of secondary school teachers in KwaZulu-Natal province
Bibliographic record
Abstract
The rapid integration of technology into various aspects of society, driven by the Fourth Industrial Revolution, has transformed the field of education. While technology is recognized as an essential component of teaching and learning, not all teachers embrace it with enthusiasm. Technophobia, or the fear of using technology, can hinder teachers from effectively integrating technology into their teaching practices, potentially impacting student learning outcomes. This study aims to examine the underlying factors of technophobia among teachers and explore the implications for technology integration in education. This study employed a quantitative approach, with data collected from 150 teachers in Pietermaritzburg using structured questionnaires. Data collected was analysed using Statistical Package for Social Sciences. The findings reveal a significant prevalence of technophobia among teachers, with fear, anxiety, and avoidance towards technology being reported by a considerable number of respondents. Lack of technological proficiency, fear of change, perceived complexity of technology, and concerns about privacy and security were identified as contributing factors to technophobia. The findings further reported that age and level of education does contribute to technophobia among teachers. These findings underscore the pressing need for targeted professional development. As a result, this study recommends that the department of basic education to provide funding for professional development programmes to train in-service teachers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".