Analysis of the Opinions of Social Studies Teachers on Digital Literacy Skills
Bibliographic record
Abstract
Thanks to digital literacy skills in Social Studies program, it is purposed to provide students with easy access to accurate and reliable information among complex information masses. Digital literacy skill not only provides accurate access to information, but also enables students to study on a legal level, in compliance with ethical rules. In addition, digital literacy makes it easier to use information accessed in daily life, to rearrange information, to generate new information, and to critically evaluate information. In this context, it is expected from a digital literate individual to acquire some knowledge and skills such as using cognitive skills at the highest level, using technological tools in a useful and effective way, accessing information and documents easily, and carrying out this through ethical rules. Social studies course, which plays a key role in providing students with digital literacy skills. In this sense, it contributes to critical and analytical thinking of students by equipping them with cognitive, affective, and social skills. The purpose of this study is to determine the opinions of teachers on digital literacy skills in social studies program. Research group consists of 50 Social Studies teachers working within Ministry of National Education in the provinces of Muş, Malatya, Elazığ, and Şanlıurfa in 2019-2020 academic year. The opinions of teachers were obtained through semi-structured interview form. The obtained data were analyzed by using qualitative data analysis technique. As a result of this study, it was concluded that social studies teachers have different perceptions of digital literacy skills, and some recommendations were provided in the light of findings.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".