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Record W2598000351 · doi:10.5539/ies.v10n4p160

Basic Technology Competencies, Attitude towards Computer Assisted Education and Usage of Technologies in Turkish Lesson: A Correlation

2017· article· en· W2598000351 on OpenAlexvenueno aff
Serpil Özdemir

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishInformation technologyPsychologyMathematics educationTest (biology)Computer technologyMedical educationPedagogyComputer scienceMultimediaMedicine

Abstract

fetched live from OpenAlex

The present research was done to determine the basic technology competency of Turkish teachers, their attitude towards computer-assisted education, and their technology operation level in Turkish lessons, and to designate the relationship between them. 85 Turkish teachers studying in public schools in Bartin participated in the research. The research was designed with relational screening model. The results obtained in the research are as follows: The information technology competency of Turkish teachers was determined as high-level in technologies usually used vocationally, and as mid-level in others The Turkish teachers had high-level of attitude towards computer-assisted teaching. However, they tended to use technology in their lessons at mid-level. The dependent variables did not indicate remarkable differences based on gender, teaching experience, and education level. Results showed that there was a high-level relationship between the attitude towards computer-assisted education and using information technology in Turkish lessons. Also, there existed a mid-level relationship between the basic technology competency and attitude towards computer-assisted education.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.422
Teacher spread0.346 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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