Bibliographic record
Abstract
While internet capable technology (ICT) use integrated within the curriculum has been linked to higher test scores, better GPAs and greater learning goal achievement (Kay & Lauricella, 2014), technology use does not always enhance learning. Within learning environments many students use ICT for off-task activities, and this is referred to as media-multitasking (Ophir, Nass, & Wagner, 2009). Unless two tasks are simple and well practiced, people show diminished attention and performance capabilities whilst multitasking due to cognitive limitations. Within educational contexts this explains why higher levels of media-multitasking have been associated with poorer academic performance and lower GPAs (e.g., Bowman, Levine, Waite, & Gendron, 2010). Given the significant implications of students’ media-multitasking for their learning outcomes, it is important to understand what media-multitasking activities are undertaken within learning contexts. The current study presents data examining the association between students’ media-multitasking within academic contexts (lectures, tutorials, exam study, assignment writing and recorded lecture viewing), and their attention and memory skills. Across all academic contexts, higher levels of media-multitasking were associated with more mental errors, more attentional focus and memory problems, and more mind wandering. Students reported more media-multitasking during assignment writing and exam study than when at class or viewing recorded lectures. The cognitive consequences of media-multitasking within learning environments will be discussed (e.g., increased task difficulty, memory load and switching between tasks) and the Cognitive Load Theory (Van Merrienboer & Sweller, 2005) will be used to illustrate why media-multitasking interferes with learning. Given the duty of care of educators for student learning, strategies for educating and regulating student media-multitasking behaviours within academic learning environments (e.g., technology use rules, engaging classes, active learning and educational activities, Hayashi, & Nenstiel, 2019, Purwaningtyas, 2019) will also be discussed.
 
 References
 Bowman, L. L., Levine, L. E., Waite, B. M., & Gendron, M. (2010). Can students really multitask? An experimental study of instant messaging while reading. Computers & Education, 54(4), 927-931.
 Hayashi, Y., & Nenstiel, J. N. (2019). Media multitasking in the classroom: Problematic mobile phone use and impulse control as predictors of texting in the classroom. Current Psychology, 1-7.
 Kay, R. H., & Lauricella, S. (2014). Investigating the benefits and challenges of using laptop computers in higher education classrooms. Canadian Journal of Learning and Technology, 40(2), n2.
 Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 15583-15587. https://doi.org/10.1073/pnas.0903620106
 Purwaningtyas, I. (2019). Pursuing Effective Media Multitasking: An Effort of Managing Distractions in Digital Learning Classrooms.
 Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147-177.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".