Doodle Away: Exploring the Effects of Doodling on Recall Ability of High School Students
Bibliographic record
Abstract
Doodling is often misinterpreted as a distraction to students in an academic setting– a hindrance to learning. However, recent research has shown that doodling may be beneficial to learning and memory retention. The current study expands upon previous research by investigating the impact of structured and unstructured doodling on auditory recall. This experiment was designed using a multi-method quantitative approach with an experiment that consisted of a control, structured doodling, and unstructured doodling group, and a questionnaire to assess students’ doodling experience. A group of 39 high school juniors were chosen for this study. In all three conditions, students listened to a history lecture in their normal classroom circumstances and took a quiz over the information afterward. Students doodled in both experimental conditions– they shaded a structured doodling sheet in the first condition and doodled in a blank, white A4 sheet in the second condition. The results indicated that those in the structured and unstructured doodling group performed significantly better than those in the control group, with structured doodling scoring the highest out of the three. The Post Doodling Questionnaire indicated that the majority of students experienced less daydreaming and increased recall while doodling; furthermore, the majority of students reported doodling naturalistically.
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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 | 0.000 | 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) | 0.001 | 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".