Mind and mood in harmony: synergizing cognitive efficiency and emotional engagement across different modalities of online learning resources
Bibliographic record
Abstract
Online learning resources are crucial as they bridge physical distance between learners and instructors, making both cognitive efficiency and emotional engagement vital for effective learning. This study investigates how different modalities of online learning resources (words only, words & sounds, words & pictures, and videos) affect students’ cognitive load, learning emotions, and satisfaction. 107 first-year high school students were randomly assigned to the four multimedia modalities to learn how to conduct a chemistry experiment. The results revealed significant differences in cognitive load and learning performance across four modalities. The “words & pictures” group had the best learning performance, reported the lowest levels of mental load, invested the most mental effort, and experienced more positive emotions, whereas the “videos” group reported the highest levels of cognitive load, and the “words only” group experienced the most negative emotions. Learning emotions were positively correlated with performance, highlighting the importance of both cognitive and emotional factors in multimedia instructional design. These findings suggest that balancing cognitive load and eliciting positive emotions in online learning materials enhances effectiveness. Combining visuals with text and enhances information controllability, as in the “words & pictures” modality, proved to be the most beneficial approach.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".