Split‐Attention and Coherence Principles in Multimedia Instruction Can Rescue Performance for Learners with Lower Working Memory Capacity
Bibliographic record
Abstract
Summary This study examined the relation between working memory capacity (WMC) and the principles of Split‐Attention (Experiment 1) and Coherence (Experiment 2) in multimedia learning. Split‐Attention refers to reduced comprehension when learners must divide their attention between images and text, and Coherence refers to reduced comprehension when learners must process irrelevant information. In Experiment 1, those with lower WMC performed worse compared with those with higher WMC when learning from the Split‐Attention condition (audio + on‐screen text + images), but not when learning from the Complementary condition (audio + images). In Experiment 2, those with lower WMC performed worse compared with those with higher WMC when learning from the Incongruent condition (audio + irrelevant images), but not when learning from the Congruent condition (audio + relevant images). Findings reinforce the importance of pedagogically sound instructional design, as it may especially benefit those with lower WMC and equate learning across working memory abilities. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".