How collaboration influences the effect of note-taking on writing performance and recall of contents
Bibliographic record
Abstract
Note-taking is a commonly applied pedagogical strategy across all areas of education. In higher education specifically, there has been an increasing push to get students involved in collaborative note-taking in order to increase their engagement with the contents and to inspire deeper and more meaningful learning. However, there is a lack of clarity as to whether collaborative note-taking positively influences student performance. For this reason, the present study (n = 189) compares the learning performances of students in a collaborative note-taking condition to those of students in an individual note-taking condition. The students were compared in regards to their retention of information and their performance on academic writing. The study found that students from the collaborative note-taking group performed better on measures of retention, while the individual note-taking group performed better on measures of academic writing. These results suggest that while the collaborative processes of group note-taking lead students to retain more information, these processes do not lead to better performance in academic writing. The present study fills a gap in the research by showing how the effectiveness of collaborative note-taking might depend on the learning context or on the desired result of the class.
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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 | 0.003 | 0.048 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".