Exploring the Theoretical Foundations, Claims, and Caveats of the Design Principles for Multimedia Learning in Higher Education
Bibliographic record
Abstract
Multimedia learning takes place when people learn from images and words together. Research and theory from cognitive psychology, social psychology, and instructional design have all been used to propose and investigate specific design principles for effective multimedia learning. Research has demonstrated that applying even one of these principles can have an enormous impact on participants’ learning outcomes, producing large effect sizes on tests of memory and transfer or application of knowledge. The literature suggests that applying multiple principles at once will produce tremendous benefits for student learning and motivation. But while courses are messy and complicated, most of the research on multimedia design principles has been conducted in highly controlled, one-session experiments testing one principle at a time. Curriculum and instructional design both involve a multitude of variables. What impact do these principles have in the context of all of the other decisions that instructors make? This review makes a strong case for increasing the external validity of research on multimedia design to understand its role in higher education, pointing to classroom-based research as a natural and necessary next step.
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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 | 0.049 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.048 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".