English Language Exposure and Literacy Rate toward Language Proficiency: A Cross-country Analysis
Bibliographic record
Abstract
Globalization has made English more important than ever. Through time, curriculum designers and teacher practitioners remain steadfast in finding ways to advance the quality of student learning. To ascertain the quality of language teaching and learning, parameters like standardized tests are set. This paper examined, at the cross-country level, the difference between the 2009 and 2013 Test of English as a Foreign Language (TOEFL) iBT scores and the effect of language exposure on the test takers’ scores. It further investigated the correlation between literacy rate and English language use in the scores obtained. Using paired t-test to determine the English proficiency of the test takers and Pearson r to test the correlation of the literacy rate and language use in the scores obtained, the findings showed a significant difference in the mean scores between 2009 and 2013 scores in TOEFL. The results also revealed a strong positive linear relationship between TOEFL scores and literacy rate, while no association exists between TOEFL scores and language exposure. The quality of comprehensible input is more important than the quantity of language exposure. Active immersion in a language is still an acknowledged fact that contributes to effective language learning. Literacy remains a foundational competency that is of primary importance to language learning. It is then imperative that schools revisit language learning curricula and emphasize quality instruction through authentic language tasks and activities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".