Online text processing: A study of Iranian EFL learners’ vocabulary knowledge
Bibliographic record
Abstract
The internet has developed into an important source of knowledge in recent times. It is used not just for engaging and entertaining users, but also for promoting language learning, especially for English as a Second/Foreign Language (ESL and EFL) learners spending long hours using internet, 85% of all web pages are in English. This experimental research investigated EFL learners’ experiences of vocabulary learning while surfing and text processing. In this small-scale study, two homogeneous groups of EFL learners )N=19(, after taking a vocabulary test to ensure that their vocabulary knowledge differences were not significant, were randomly assigned to attend Interchange 3 class in two different groups – one as the Experimental and the other as the Control Group. Each session, there was a free discussion on special topics; while the Experimental Group surfed the internet, processed the online texts, shared and discussed their findings and beliefs on the internet, the Control Group did not use the internet and simply shared their opinions and discussed their personal beliefs. The results of the vocabulary pre- and post-tests indicated that the “internet users” significantly outperformed the “non-internet users”, that is, the Control Group. Based on the findings, internet creates a stimulating environment which helps learners effectively boost their vocabulary knowledge.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".