Teacher Perception on Educational Informatics Network: A Qualitative Study of a Turkish Anatolian High School
Bibliographic record
Abstract
FATIH Project carried out by the Turkish government is one of the comprehensive technology integration project in the World. With this project, interactive boards, tablets and multifunctional printers have been distributed to schools and Internet infrastructure of schools improved. EIN (Educational Informatics Network) platform, known as EBA (Egitim Bilisim Agi) in Turkey, has been established to access the digital content to be used in conjunction with this technology. In this qualitative study, the process of using EBA was examined by benefiting from teachers’ opinions and experiences; and it was aimed at revealing the problems experienced by teachers while using EBA and opinions regarding the alternatives for existing problems. Based on the content analysis of teachers’ answers, it was concluded that problematic conditions as insufficient systematic structure and content of EBA, inappropriate content for students’ needs and grade level, incompatibility with the changes in instructional programs, central exams pressure, and students’ concentration on teacher-oriented approach had negative impacts on teachers unwilling and/or insufficient EBA use. Therefore, a model was presented to include teachers’ recommendations which are composed of three strategies for the solution of the problems they experienced while using EBA.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".