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Record W2755636397 · doi:10.5539/hes.v7n4p1

Evaluation of Electronic Writing Experiences of Turkish Teacher Candidates at WATTPAD Environment

2017· article· en· W2755636397 on OpenAlexvenueno aff
Talat Aytan

Bibliographic record

VenueHigher Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishHandwritingSpellingPsychologyMathematics educationGrammarPedagogyLinguistics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.400
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2017
Admission routes1
Has abstractyes

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