Why are graduate students' note-taking practices noteworthy?
Bibliographic record
Abstract
Although note-taking is generally seen as vital to students’ academic success, students are normally left to find their own way in mastering note-taking (Blair, 2004; Coullie, 2020; Lessa et al., 2022), particularly within graduate studies (Fine et al., 2021; Wohl & Fine, 2017b). Most of the literature discussing note-taking centres on undergraduates and classroom listening contexts (Fine et al., 2021). However, there is need to investigate graduate note-taking practices from reading away from the classroom, as assumptions and expectations that all students entering graduate studies are/should be proficient in graduate-level reading is unrealistic (Liu & Pullinger, 2021) and unreasonable (Reid, 2018; Wohl & Fine, 2017b), particularly considering the diverse cultural and linguistic backgrounds typifying graduate student enrolment across North American universities. This textographic study explores and analyzes the note-taking practices of a small sample of graduate students enrolled in an Atlantic Canada university, drawing from Academic Literacies theory, particularly Lillis’ (2008) notions of how people’s “talk around text” index or point to the wider social context and people’s orientation (beliefs/values/attitudes) to their textual practices. While each student developed note-taking strategies that helped them to cope with the large volumes of readings, and the depth of critical reading required, an individual unfamiliar with Western academic writing norms can be at a distinct disadvantage in avoiding charges of plagiarism. While the literature on note-taking privileges generative note-taking strategies, there is greater need for appreciation for how non-generative note-taking strategies are efficacious for some students. In addition, while this study resists seeing students’ note-taking practices in deficit terms, instead presenting the students’ own assessments of their note-taking practices, a case is being made for further developing students’ reading proficiencies through explicit instruction in note-taking.
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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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".