HOW DOES PRIOR WORD KNOWLEDGE AFFECT VOCABULARY LEARNING PROGRESS IN AN EXTENSIVE READING PROGRAM?
Bibliographic record
Abstract
Sixty English as a foreign language learners were divided into high-, intermediate-, and low-level groups based on their scores on pretests of target vocabulary and Vocabulary Levels Test scores. The participants read 10 Level 1 and 10 Level 2 graded readers over 37 weeks during two terms. Two sets of 100 target words were chosen from each set of graded readers and were tested on three occasions. The results showed that the relative gains from pretest to immediate posttest were 63.18%, 44.64%, and 28.12% for the high-, intermediate-, and low-level groups, respectively. There was little decay in knowledge on the Term 1 three-month delayed posttest; relative gains ranged from 21.05% for the low-level group to 59.01% for the high-level group. The learning gains in Term 2 were consistent with those from Term 1. The results indicate that prior vocabulary knowledge may have a large impact on the amount of vocabulary learning made through extensive reading.
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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 | 0.006 |
| 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".