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Record W2552202966 · doi:10.5539/ijel.v6n6p175

The Effect of Using Cell Phone Dictionary on Improving Male and Female Iranian EFL Learners’ Spelling

2016· article· en· W2552202966 on OpenAlexvenueno aff
Amir Reza Nemat Tabrizi, Mohammad Bagheri Fard

Bibliographic record

VenueInternational Journal of English Linguistics · 2016
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
Fundersnot available
KeywordsSpellingPhoneTest (biology)Session (web analytics)Analysis of variancePsychologyHomogeneousRepeated measures designMathematics educationComputer scienceMathematicsStatisticsLinguisticsBiologyWorld Wide Web

Abstract

fetched live from OpenAlex

<p>This paper attempts to investigate the effect of using cell phone dictionaries on improving male and female Iranian high school EFL learners’ spelling. To this end, ninety participants were randomly selected from Dinodanesh and Etrat high schools in Lordegan city, and they were given a spelling pretest based on their course book. Then sixty subjects whose scores were within the range of one standard deviation above and below the mean were selected as homogeneous and divided into experimental and control groups. Using cell phone dictionaries was conducted in eight class session, during which experimental group received training with cell phone dictionaries while the control group only received training without any cell phone dictionaries. The data were collected via a pretest and a posttest. The analysis of the test scores using t-test revealed that the experimental group did statistically better in the test. The results revealed that using cell phone dictionary had positive effect on improving EFL learners’ spelling. At the end, a two-way ANOVA was run to compare the two groups plus the effect of gender on such performance. The results indicated that treatment have an effect on the improving of both male and female EFL learners’ spelling.</p>

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)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.018
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 science0.0010.000
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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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.

Study designOther design
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

Citations1
Published2016
Admission routes1
Has abstractyes

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