Meta-analysis on Element of Cognitive Conflict Strategies with a Focus on Multimedia Learning Material Development
Bibliographic record
Abstract
Multimedia materials are becoming more commonly used in curricula. Multimedia learning tools that integrate text, graphics, audio, video, and animation make learning more interesting and easier for understanding a concept. These tools have been used in different ways over the years to support student learning in all branches of education. Diverse teaching strategies have been adopted in developing multimedia learning materials in many interesting designs. These strategies are designed to achieve a number of objectives. One of them is to overcome misconception among the students. Theoretically, misconception is a point at which students have understood certain concepts in the wrong manner. Usually, those students who are in this situation refuse to switch to the right one. Cognitive conflicts strategy is a part of psychological theories of conceptual change. This strategy is effective in correcting a misconception as well as in improving performance. Once an unreliable event is mismatched with the preconception held by the student, cognitive conflict will take place. The student will engage with the learning material and reconstruct his or her concepts to overcome conflict. There has been a lot of researches related to cognitive conflict strategy in Science and Mathematics education. This strategy has been demonstrated to improve students’ performance and misconception. Still, a lot of strategies have been implemented through face-to-face classroom instruction. With the growth of multimedia resources, a cognitive conflict strategy is believed to be employed when developing multimedia learning material. Even so, which elements of cognitive conflict strategy are usable within multimedia learning materials are still an ongoing inquiry. This research attempts to investigate elements of cognitive conflict strategy that could be embedded within multimedia learning materials that might effectively overcome the students’ misconception based on detailed literature review using meta-analysis technique. After being analysed qualitatively, five elements of cognitive conflict strategy have been identified: (1) meaningful information; (2) challenging students’ existing concept; (3) ability to gain attention, (4) motivation, and (5) comfortability in using the multimedia learning materials.
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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 | 0.058 | 0.184 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.052 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".