Active vs passive media multitasking and memory for lecture materials
Bibliographic record
Abstract
Abstract Access to digital technology in the 21st century has led to the emergence of media multitasking (MMT), which involves attempting to engage with multiple streams of media at the same time. This behaviour, which is frequently considered to be a form of inattention, has become increasingly prevalent in educational settings, such as undergraduate lectures. The aim of the present study was to examine volitional media-multitasking (MMT) during an asynchronous online lecture by giving participants the opportunity to engage with a secondary, non-required media stream (i.e., the game of snake). Participants (n = 222) were randomly assigned to either an Active condition, in which they could play the snake game using the arrow keys; or a Passive condition, in which they could watch the snake game, but could not play it. In both conditions, participants could toggle the snake game on and off, using a keypress. MMT was indexed behaviourally by measuring the percentage of time participants had the secondary stream toggled on (i.e., snake time percentage), a method pioneered by Ralph et al. (2020), and subjectively by asking participants to what extent they engaged with other media while the lecture was playing. Following the lecture, participants completed a multiple-choice quiz and self-reported their level of MMT. Our behavioural measure (i.e., snake time percentage) indicated that participants spent significantly more time MMT in the Active condition than the Passive condition. However, there were no significant differences in self-reported MMT or quiz performance across conditions. Furthermore, correlations between both measures of MMT and quiz performance were non-significant. Thus, the present study found no performance decrement as a result of, or in association with, increased volitional MMT.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".