Aspects Affecting the Use of Digital Technologies in Greek Schools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper is analyzing feedback collected from Greek high school teachers, in order to answer questions regarding the use of Information and Communication Technologies (ICT) as part of their teaching practices in Greek schools. Various hypothesis tests are presented, in order to focus on perceptions, look into possible problems and see how teachers of different profile and specializations apply ICT in their teaching. A special focus is placed on philogists, as the most populous but also less technology-related subject, content-wise. In particular, an online survey was prepared, aiming at collecting information regarding the use of ICT in Greek schools. This information is analyzed across various demographic and profession-related variables concerning teacher’s age, gender, specialization, type of technology used, familiarization with ICT, motivations. A total of 309 respondents reacted to the questionnaire, over a period of about two months in 2019. The questionnaire consisted of 31 questions in total. The analysis that follows is both descriptive and statistical, while it intends to provide answers and correlations regarding the most striking questions and hypotheses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it