Investigation of Vocational High School Students 'Views on Smart Phone Use: A Case Study
Bibliographic record
Abstract
The most preferred tools of technology today are internet and smart phones. Nowadays, these two tools offer numerous services and facilities to humanity in many areas. Accessing technology at any time, having a pleasant time, interacting without the limitations of face to face communication are some of them. That is why; individuals have become unable to live without internet and smartphones. Especially the rapid developments in information and communication technologies, internet and social media media, which is among the means of access to smart phones among university students and has made widespread use. In this context, a case study was conducted in order to examine the opinions of the students about the use of smart phones in Vocational High School students. For this purpose, the students' opinions were taken with a structured interview form. The questions in the interview form were developed by the researchers by conducting a comprehensive literature review. Expert opinion was consulted in the preparation of the interview form. The experts evaluated the suitability of the questions in terms of scope and language. The study group consisted of 10 students from the Department of Computer Programming and Computer Technology of Kafkas University. It is foreseen that the results of the research can lead to future scientific studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".