Multitasking in the classroom: Testing an educational intervention as a method of reducing multitasking
Bibliographic record
Abstract
Increasingly, students engage in multitasking during lecture by shifting their attention between class material and irrelevant information from texts and webpages. It is well established that this divided attention impairs memory and learning. Less is known about how to correct the problem. This study used an educational intervention in the form of a PowerPoint presentation that informed students in the experimental condition about the deleterious effects of multitasking. Students were randomly assigned to the experimental condition, the placebo condition (a slideshow about sleep), or no intervention. Participants self-reported the percentage of the time they multitasked in class and paid attention at two time points, baseline (before the intervention), and in a second lab visit 3 weeks later. The experimental intervention did not reduce student multitasking or increase student attention, relative to the other conditions. Supplementary research questions examined students’ beliefs about multitasking, finding that most thought it decreased their grades. The correlations between grade point average, stress, and boredom proneness, on one hand, and baseline attention and multitasking in class, on the other, were also inspected, revealing that students with higher grade point average pay more attention in class and multitask less. Suggestions for future research to reduce multitasking are made, including having students engage in multitasking to observe the effect on their memory retention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".