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Record W2165509962 · doi:10.5539/elt.v5n4p108

Multivariate Effects of Level of Education, Computer Ownership, and Computer Use on Female Students’ Attitudes towards CALL

2012· article· en· W2165509962 on OpenAlexvenueno aff
Mehrak Rahimi, Samaneh Yadollahi

Bibliographic record

VenueEnglish Language Teaching · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyExhibitionPositive attitudeValue (mathematics)Social psychologySample (material)Mathematics educationMathematics

Abstract

fetched live from OpenAlex

The aim of this study was investigating Iranian female students’ attitude towards CALL and its relationship with their level of education, computer ownership, and frequency of use. One hundred and forty-two female students (50 junior high-school students, 49 high-school students and 43 university students) participated in this study. They filled in A-CALL questionnaire that assessed their attitudes towards CALL with respect to four factors: effectiveness of CALL vs. non-CALL, surplus value of CALL, teacher influence, and degree of exhibition to CALL. The findings revealed that the sample had a general positive attitude towards CALL while they showed the highest positive attitudes towards teacher influence and the lowest positive attitudes towards effectiveness of CALL vs. non-CALL. Students’ attitude toward CALL across level of education was found to be significantly different just in degree of exhibition to CALL; while university students had the highest level of positive attitudes in this regard. Further, computer ownership could make a difference in students’ attitude towards teacher influence. No statistically significant difference was found between the attitudes of those students who used computer more frequently and those students who did not use it quite often.

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)0.001
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 score0.571
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.053
GPT teacher head0.367
Teacher spread0.314 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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