The Influence of Electronic Dictionaries on Vocabulary Knowledge Extension
Bibliographic record
Abstract
<p>Vocabulary learning needs special strategies in language learning process. The use of dictionaries is a great help in vocabulary learning and nowadays the emergence of electronic dictionaries has added a new and valuable resource for vocabulary learning. The present study aims to explore the influence of Electronic Dictionaries (ED) Vs. Paper Dictionaries (PD) on vocabulary learning and retention of Iranian EFL learners. Seventy college students formed the participants of the study. Before the treatment, a Preliminary English Test was used for assessing the participants’ homogeneity. The participants were assigned to Electronic Dictionary (ED) group and Paper Dictionary (PD) group. The treatment lasted for 15 sessions. Eighty-eight new target words were selected in order to be taught in this study. The ED group participants were asked to use their mobile dictionary (Blue Dict dictionary), that include eight popular different dictionaries. The participants of the PD group used their ordinary Paper Dictionaries for finding the meaning of words. In order to check their short-term and long-term vocabulary learning, both groups took part in an immediate and delayed post-test respectively after the treatment. Based on the t-test results, the participants in ED group outperformed those of PD group. The overall results indicate that EDs can improve vocabulary learning.</p>
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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 | 0.001 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".