Second language vocabulary learning through extensive reading with audio support: How do frequency and distribution of occurrence affect learning?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it