An Investigation of Accuracy of Metacognitive Judgments during Learning with an Intelligent Multi-Agent Hypermedia Environment
Bibliographic record
Abstract
Successful learning with advanced learning technologies is based on the premise that students adaptively regulate their cognitive and metacognitive processes.However, research suggests that students are rather dysregulated in their learning.One major source of dysregulation is based on inaccurate metacognitive judgments made during learning.This study investigated learners' accuracy and confidence in metacognitive judgments made in the context of learning about the human circulatory system with MetaTutor, a multiagent intelligent hypermedia learning system.83 college students took part in this study, and their interactions within MetaTutor in the two-hour learning session provided data for this study.In general, the results revealed that learners were overconfident to differing degrees in ratings of their judgments of learning (JOLs) and feelings of knowing (FOKs).It was also found that receiving timely prompts and adaptive feedback from the artificial agent in MetaTutor improved the accuracy of metacognitive judgments.Learners in the Prompt and Feedback condition (PF) were overconfident to a lesser degree than those in other conditions (Prompt Only [PO] and Control).Finally, one-way ANOVA and Tukey post-hoc results indicated that learners who received prompts and feedback attained significantly (p < .05)better learning efficiency scores than learners in Control and Prompt Only conditions.
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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 | 0.002 | 0.017 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".