Evaluation of Electronic Writing Experiences of Turkish Teacher Candidates at WATTPAD Environment
Bibliographic record
Abstract
The purpose of this study is to analyze Turkish teacher candidates’ electronic writing experiences at wattpad.com environment. The study group of this research consisted of 53 Turkish teacher candidates who were studying at a state university in Istanbul. Teacher candidates in the study group joined Wattpad.com and wrote at least one narrative text and informative text within a month. A structured interview form was used to receive the opinions of 15 teacher candidates who experienced writing in electronic environment. The data obtained using the interview form were subjected to content analysis. The electronic writing experiences of Turkish teacher candidates were interpreted in the framework of the themes which were formed on the basis of advantages and disadvantages. Prospective Turkish teachers evaluated the writing in electronic environment as advantageous in terms of legibility and spelling check, reader and writer interaction and visual appeal, time saving and convenience, affordability, quick feedback and constructive criticism, encouragement, archiving possibilities and socialization. On the other hand, they considered the writing in electronic environment as disadvantegous in terms of unreliability of virtual world, distractibility, severe criticisms, not comparable to handwriting, character limitation, health concerns, wording concerns, asociality, copyright and plagiarism concerns, using profanity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".