Note-taking for the win: Doodling does not reduce boredom or mind-wandering, nor enhance attention or retention of lecture material
Bibliographic record
Abstract
Abstract Doodling and fidgeting—traditionally viewed in educational contexts as markers of inattention and poor classroom behaviour—have more recently been considered as possible routes to improve performance by reducing boredom and its negative impact on memory. However, there is a surprising lack of well-controlled studies examining this possibility, despite the widespread adoption of fidget toys and doodling exercises within classroom settings. Here we report two experiments (total N = 222) that assess the impact of doodling on boredom, attention, mind-wandering, and subsequent recall of auditory information. In Experiment 1, participants first listened to a 15-minute section of a lecture known to induce boredom. Immediately thereafter they were asked to jot down important information from a short voicemail that they listened to while either doodling (adding shading to shapes) or doing nothing in between note-taking. In Experiment 2, participants listened to a 45-minute section of the same lecture under one of four conditions: structured doodling (i.e., shade in shapes), unstructured doodling, note-taking, or listen-only. Thought probes assessed self-perceived levels of state boredom, mind-wandering, and attention throughout the lecture. Across studies, doodling neither reduced boredom or mind-wandering nor increased attention or retention of information compared to other conditions. In contrast, attention and test performance were highest (and boredom and mind-wandering lowest) for those focused solely on 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".