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
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".