The Effect of Taking Different Types of Breaks During Online Lectures on Student Attention and Learning
Bibliographic record
Abstract
Attention during lectures often declines due to high cognitive demands, challenging student learning. The increasing popularity of online education, especially since the COVID-19 pandemic, has added to the attention-sustaining difficulties students face during lectures. Online lecture breaks are a promising intervention to enhance attention and learning; however, there is limited research on how they should be designed. In the current study, I investigated the effects of break duration and frequency (Experiment 1) and break activity (Experiment 2) during an online lecture on student attention and learning. In both experiments, undergraduate students watched a 50-minute video-recorded online lecture and then completed an immediate comprehension quiz and a post-lecture survey that included questions about their lecture engagement and experiences. In Experiment 2, attention was measured through probes during the lecture, and a second comprehension quiz was administered one week later. Contrary to my predictions, findings from Experiment 1 revealed that taking three 2-minute open-ended lecture breaks led to significantly lower performance on the immediate quiz compared to taking one 6-minute open-ended lecture break or no breaks. Interestingly, participants in the combined break(s) conditions reported engaging in significantly more media multitasking behaviour and decreased levels of motivation during the lecture compared to the no breaks condition. Consistent with my predictions, Experiment 2 demonstrated that taking stretching breaks during an online lecture significantly improved immediate quiz performance compared to taking social media breaks or no breaks. These findings suggest that structuring online lectures with well-designed breaks can enhance learning outcomes, with stretching breaks showing particular promise. However, further research is needed to explore other factors that influence the quality and effectiveness of lecture breaks, as poorly designed breaks may inadvertently hinder learning.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".