Exploring the Theoretical Foundations, Claims, and Caveats of the Design Principles for Multimedia Learning in Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 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 | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it