Exploring the impact of note taking methods on cognitive function among university students
Bibliographic record
Abstract
BACKGROUND: Taking notes during lectures plays a vital role in enhancing learning outcomes. With technological advancements, digital note-taking has gained popularity among university students in recent years due to its convenience, ease of storage, sharing, and searching. Different versions of digital note-taking have been introduced, including the use of styluses on tablets, which offer a blend of traditional handwriting and digital advantages. However, the use of digital devices may introduce distractions, such as access to social media, potentially disrupting focus and impacting learning effectiveness. Therefore, their impact on learning and cognition remains a topic of ongoing exploration. This study aimed to investigate the differences in cognitive functions between university students practicing either longhand or styluses digital note-taking methods in the United Arab Emirates. METHODS: One hundred students participated in this cross-sectional study. Sociodemographic information, including age, sex, nationality, and study year were obtained. Participants reported the note-taking method they use (longhand vs. digital note-taking with styluses). A battery of cognitive tests was used in this study to assess different cognitive functions, including the Montreal Cognitive Assessment (MoCA), the Symbol Digit Modalities Test (SDMT), the Brief Visuospatial Memory Test-Revised (BVMT-R), and the Stroop Color and word test. The Mann-Whitney U tests were used to assess differences in different cognitive domains between participants following longhand and styluses digital note-taking. RESULTS: Students that used longhand note-taking demonstrated significantly higher overall cognitive scores (MoCA, p = 0.005), along with superior information processing speed, working memory (SDMT, p = 0.045), and better visual memory (BVMT-R, p = 0.01), compared to those who used styluses digital note-taking. However, students using styluses digital note-taking exhibited better inhibitory cognitive control (Stroop test, p = 0.020). CONCLUSIONS: Although using styluses offers a hybrid experience by combining the tactile benefits of handwriting with the digital advantages of electronic devices, students who employed longhand note-taking demonstrated significantly higher cognitive scores across several domains compared to their peers using stylus-based digital methods. However, while these differences were statistically significant, the effect size was small. Longitudinal cohort studies are needed to further examine the predictive, mediating, and confounding factors related to note-taking methods and cognitive abilities in students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".