Bibliographic record
Abstract
The present study aims at investigating the role of different technologies which can provide authentic language input for language learning in EFL context. As the matter of fact, the study focuses on different technologies as sources of language input in EFL contexts which lack social interaction as an established source of language input in ESL context. In this regard, a study was conducted with the help of twenty language learners in Iran and twenty language learners in Malaysia. During the study, language learners in Iran used different technologies as authentic source of language input for language learning. On the contrary, the language learners were asked to use the social interaction as a source of language input. The results of the post-test indicated a significant improvement in language proficiency of those who used technology. Key words: Technology; Language input; Social interaction; Language proficiency Resume: La presente etude vise a etudier le role de differentes technologies qui peuvent apporter une entree de langue authentique pour l'apprentissage des langues dans le contexte ALE(Anglais langue etrangere). En fait, l'etude se concentre sur de differentes technologies comme des sources d'entree de langue dans des contextes ALE qui manquent de l'interaction sociale. A cet egard, l'etude a ete menee avec l'aide de vingt apprenants en Iran et vingt apprenants en Malaisie. Au cours de l'etude, les apprenants en Iran ont utilise de differentes technologies comme source authentique de l'entree de langue pour l'apprentissage des langues. Au contraire, les apprenants en Malaisie ont ete invites a utiliser l'interaction sociale comme source d'entree de langue. Les resultats du post-test ont indique une amelioration significative de maitrise de la langue de ceux qui ont utilise la technologie. Mots-cles: Technologie; Langue d'entree; Interaction sociale; Maitrise de la langue
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".