The Role of Digital Technology in English Language Teaching in Azerbaijan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current study uses qualitative methodology to explore the role that digital technology plays in both second language acquisition and teaching. In-depth interviews were conducted with 6 teachers aged between 23 and 55 who are currently employed by Khazar University, Azerbaijan. Teachers indicate that the use of technology has an important impact on student’s second language learning. Although some of the teachers displayed negative effects of modern technologies on getting students’ attention, positive feedback is more available. Teachers demonstrated how the use of technology in teaching and learning supports students’ engagement in education. Overall, this study provides a reader with a general understanding of both students and teachers’ involvement in digital media as well as the effectiveness of second language teaching with technologies at higher educational institutions of Azerbaijan. Future research in the same area of study is needed to compare both teachers’ and students’ perceptions separately in broader sample and identify the key factors that affect teacher’s decision to choose rather traditional methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it