Factors Affecting Technology Integration in EFL Classrooms: The Case of Kuwaiti Government Primary Schools
Bibliographic record
Abstract
Previous Computer Assisted Language Learning (CALL) research has shown that technology is beneficial for promoting language learning, but some teachers neither use technology as an assisted tool nor integrate it into their language classrooms. It has also been argued that the integration of technology has been unsuccessful in Kuwait. This study aims to explore the factors influencing teachers’ use of technology in English as a foreign language (EFL) classroom in Kuwaiti government primary schools. More specifically, it aims to highlight factors promoting and hindering EFL teachers’ use of technology. For the purpose of this study, 55 questionnaire responses were collected from different primary school teachers in Kuwait, followed up with 15 semi-structured interviews. The study findings show that Kuwaiti primary school EFL teachers who participated in the current study demonstrated positive attitudes towards using technology and acknowledged the implementation of some cutting-edge technologies in their classrooms. Enhancing students’ language learning, innovation and school support were the main factors that encouraged the participating teachers to use technology. Other factors hindered the use of technology; particularly those related to lack of parental support, personal expenses and health problems, teachers’ lack of skills and training, poor classroom infrastructure, and time constraints/workload. The findings also reveal that the lack of professional development training workshops provided by the Ministry of Education led the Kuwaiti teachers to rely more on informal training in which they worked and learnt together with their colleagues in small sub-groups to improve their use of technology. The study findings have implications for policymakers and other stakeholders intending to integrate technology in Kuwaiti primary schools.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".