Second language vocabulary learning through extensive reading with audio support: How do frequency and distribution of occurrence affect learning?
Bibliographic record
Abstract
This study investigated (1) the extent of vocabulary learning through reading and listening to 10 graded readers, and (2) the relationship between vocabulary gain and the frequency and distribution of occurrence of 100 target words in the graded readers. The experimental design expanded on earlier studies that have typically examined incidental vocabulary learning from individual texts. Sixty-one Taiwanese participants studied English as a foreign language (EFL) in an extensive reading program or in a more traditional approach structured around a global English course book. A pretest, posttest, and delayed posttest were administered to all participants. The results indicated that vocabulary gains through reading and listening to multiple texts were high. Relative gains were 44.06% after reading the 10 graded readers and 36.66% three months later. The relationships between vocabulary learning and frequency and distribution of occurrence were found to be non-significant, indicating that frequency was perhaps one of many factors that affected learning.
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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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".