To what extent does reviewing notes affect L2 vocabulary learning?
Bibliographic record
Abstract
Abstract The present study investigated the extent to which reviewing notes, after viewing an academic lecture, contributes to vocabulary learning. A total of 128 Chinese university students were randomly assigned into five groups: conventional note-taking with immediate review, conventional note-taking with delayed review, guided note-taking with immediate review, guided note-taking with delayed review, and a control group. Knowledge of twenty-eight words encountered in the lecture was measured. A counterbalanced form-recall and meaning-recall test was used through pretest, posttest, and delayed posttest. Results showed that (1) immediately after the treatment, taking guided notes played a larger role in vocabulary learning over reviewing notes on both form- and meaning-recall tests; in contrast, conventional note-taking appears to depend more on reviewing notes for form-recall but not meaning-recall, (2) reviewing notes after an interval in guided note-taking contributed to significant vocabulary gains on the form-recall test. Additionally, the analyses revealed that writing unknown words, learners’ comprehension levels, and their prior vocabulary knowledge had a significant impact on learning. However, review schedule, frequency of occurrence, target words presented in guided notes, and target words shown in slides did not significantly influence 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.
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 | 0.008 |
| 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".