Teacher Education in the Digital Transformation Process in North Cyprus: A Situation Analysis Study
Bibliographic record
Abstract
While the world was already moving towards a digital future before 2020, the coronavirus pandemic accelerated that significantly in many sectors. This is certainly true with regard to digital transformation in the classroom, which gathered pace almost overnight when schools shutdown and lessons first moved online. At the time, the shift served to highlight how unprepared most of the sector was for digital transformation. At this point, both teacher and student skills and competencies for digital transformation have been questioned and many academic studies for literature have been put forward in this context. In this research, teacher education and competencies are questioned in the transition to the digital transformation process in Northern Cyprus. In addition, tools for measuring digital competencies and teacher-oriented changes will be introduced. It is thought that determining the competencies of teachers and the tools measuring these competencies within the scope of the digitalization process will be effective in ensuring quality in education on behalf of Northern Cyprus in the future and will shed light on future research. In the literature review, although the existence of studies belonging to Northern Cyprus in measuring the digital competence of teachers/teacher candidates’ is remarkable, it has been determined that there are not enough numbers according to the importance of the subject. Considering the rapid transition and adaptation to the digital transformation process, especially during the pandemic period, since teachers are the most important part of digital education, it is foreseen that more qualitative or quantitative research is needed to interpret and measure digital competencies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".