The Effect of Using Cell Phone Dictionary on Improving Male and Female Iranian EFL Learners’ Spelling
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".