Metacognitive Judgments, Study-Time Allocation and Inferences: The Effect of Multimedia Discrepancies.
Bibliographic record
Abstract
This study investigated undergraduate students' metacognitive judgments while learning about complex science topics using multimedia material (text and graph).A within-subjects design was used to examine the effect of discrepancies on study-time allocation, metacognative judgments and inference generation.There were three types of discrepancies: none, text (between two ideas in the text) and text and graph (between the text and graph).Forty (N=40) participants completed 12 trials where they were asked to provide 6 judgments: Ease of Learning judgments (EOLs), immediate and delayed Judgments of Learning (JOLs) for both text and graph and Retrospective Confidence Judgments (RCJs).Participants provided significantly lower JOLs for content that contained discrepancies but RCJs remained high across conditions.Discrepancies did not influence study-time allocation, but did significantly influence inference scores.Overall, results suggest that participants may be aware of discrepancies, but lack the control strategies needed to overcome them.
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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 | 0.008 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".