Determining the Views of Teachers on the Transition to Digital Transformation in Education During the Pandemic Process: A Case Study
Bibliographic record
Abstract
In today’s world, where people live together with technology, digitalisation is increasingly taking its place as an indispensable part of our lives. However, rapid developments and changes in technology have led to compulsory digitalisation processes in all sectors. In addition to the pandemic process, digital transformation has now become a necessity. The pandemic threat faced by countries has affected many sectors, especially the education sector. In this context, it has become a necessity to take the necessary measures in the education sector, which affects a large audience. The first of these measures is the emergency distance education plan. Undoubtedly, it is clear that every stakeholder of education is affected by the emergency distance education plan that has come with the pandemic process. The most important of these stakeholders are undoubtedly teachers. In light of all this information, this study aimed to determine the views of teachers on the transition to digital transformation in education during the pandemic process. The research is an example of a qualitative case study. A semi-structured interview form was prepared by the researcher to determine the teachers’ views on the subject. Data were collected using the semi-structured interview form. As a result, the teachers emphasised that they see the transition to digital transformation in education as a necessity, especially during the pandemic process, but they need more in-service training to keep up with this transformation.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".