Bibliographic record
Abstract
Online educational platforms have recently been used to compensate for educational activities that have been interrupted all around the world due to the pandemic during the year 2020. This study was conducted to reveal reflections of e-learning on social studies courses. Among qualitative research designs, the case study method was used. The data were collected using semi-structured interview. The study group consisted of 27 social studies teachers working in public secondary schools in Adıyaman province of Turkey. Those teachers were determined based on purposeful sampling method. These teachers were determined based on non-random purposeful sampling criterion. They were interviewed considering that they used the online education platform intensively during the pandemic and had the necessary experience to make evaluation in this regard. The data were collected between September and November 5, 2020. The data were analysed using inductive analysis. The study results showed that social studies teachers could not literally adapt to e-learning activities due to their lack of knowledge about using computers. They did not internalize virtual education but had a perception that such education could be used as a supportive one for face-to-face education or for review. The study found that the EBA (Education Information Network), the most comprehensive online educational platform in Turkey, could not literally meet the needs in terms of infrastructure and content. Teachers endeavoured to adapt to e-learning during the pandemic and they welcomed admiringly the preparations made by the MEB (Ministry of National Education) despite the deficiencies therein. The majority of teachers stated they did not receive any course related to e-learning during their undergraduate education. In-service e-learning training can be provided to social studies teachers. E-learning courses can be included in the programs in education faculties. The Ministry of National Education can enrich its infrastructure.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".