The Effect of Note-Taking Strategy Training on Passage Listening Comprehension
Bibliographic record
Abstract
This study aims at exploring the effects of note-taking strategy on passage listening performance of college students and its implications for listening teaching. For this purpose, the author carried out a study for 15 weeks in Inner Mongolia University for Nationalities. The subjects are divided into experimental class and control class. The teaching method used in the experimental class focuses on incorporating the note-taking strategy training into listening courses and the teaching method used in the control class follows the normal one without the training process of note-taking strategy. The instruments include the pre-test, the post-test, pre-questionnaire and post-questionnaire, and the data collected from the study are analyzed through SPSS17.0. The major findings show note-taking strategy has a positive effect on college students’ passage listening comprehension. After a period of note-taking training, students’ comprehensive competence in listening has improved to some extent. 2) Taking notes as much as possible is not an efficient way to get high-quality notes. The number of questions answerable from the notes is proved closely correlated with achievement of listening comprehension. The other three indices of the total number of notes, the content words notes and the notations have no significance with subjects’ quality of answer.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".