The Aims of Prospective Teachers in Using and Proficiency in Internet (As in the Sample of Pamukkale University Education Faculty)
Bibliographic record
Abstract
Internet, regardless of their volumes, brands, operating system and hardware systems, internet is now considered a meeting point for millions of computers. Today, teachers and students are gradually utilizing the net in education more and more and more frequently. Therefore, this study is a descriptive search as if aims to clarify the prospective teachers (at Pamukkale University Education Faculty) level of internet knowledge and use it. The sampling was performed through sampling group method among 780 prospective teachers. Meanwhile, the data obtained in this study was piled up through a specifically developed scale which had been built up in optical reader form. After these forms were filled in, they were scanned by the optic reader and the result obtained were analyzed through the program called SPSS 11.5 (Statistical Package for Social Sciences). According to the results the prospective teachers level of internet knowledge it was “I know with 51.2 %”. As for the prospective teachers three reasons in order are “search on the internet”, “e-mail” and “chat”.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".