"One Laptop per Child" Projects and FATIH Project: A Comparative Examination
Bibliographic record
Abstract
The aim of this study is to make a comparison between the key components of FATIH Project which its foundations was built in 2010 and the other one laptop per child projects from various countries, and to make a situation analysis in this context. Within this scope, the things that performed in the implementation process of the projects, the requirements need to be done in the context of improving conditions and relations with stakeholders, and all reflections on their learning environment are discussed based upon the main components of the FATIH Project. To that end, the projects implemented in Argentina, Austria, Brazil, Czech Republic, France, South Korea, India, Israel, Italy, Canada, Sri Lanka, Uruguay, Peru, Portugal, Rwanda, Greece and in particular to FATIH project in Turkey were examined in detail. The analyses showed that the projects were spread throughout the country without any assessment in response to the pilot studies, the lack of cooperation between agencies, companies and stakeholders in the implementation process of the projects, as well as the inadequacy of teacher training and the development of contents in most of countries. On the other hand, the factors such as lack of pedagogical and technical support in particular, is understood to cause take the use of current technologies longer than expected. It is also understood that teachers' attitudes towards technology as well as their technology knowledge and skills was not taken into consideration, and therefore the technologies in schools cannot be used effectively in these projects.Key words: FATIH project, Information technologies, E-content, Hardware infrastructure, Technical and pedagogical support, In-service training
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".