Computer‐based scaffoldings influence students' metacognitive monitoring and problem‐solving efficiency in an intelligent tutoring system
Bibliographic record
Abstract
Abstract Background Computer‐based scaffolding has been intensively used to facilitate students' self‐regulated learning (SRL). However, most previous studies investigated how computer‐based scaffoldings affected the cognitive aspect of SRL, such as knowledge gains and understanding levels. In contrast, more evidence is needed to examine the effects of scaffolding on the metacognitive dimension and efficiency outcome of SRL. Objectives This study aims to examine the role of computer‐based scaffolding in students' metacognitive monitoring and problem‐solving efficiency. Methods Seventy‐two medical students completed two clinical reasoning tasks in BioWorld, an intelligent tutoring system (ITS) designed for promoting medical students' diagnostic expertise. During solving the tasks, students were asked to report their confidence judgements about proposed diagnoses. Computer trace data were used to identify task completion time (CT) and students' use of three scaffolding types, that is, conceptual, strategic, and metacognitive. Then we calculated students' metacognitive monitoring accuracy (i.e., calibration) and problem‐solving efficiency. Results and Conclusions One‐sample t ‐test demonstrated that students inaccurately monitored their learning processes and were overconfident in both tasks. Linear mixed‐effects models (LMMs) indicated that the intensive use of metacognitive scaffolding positively predicted students' metacognitive monitoring accuracy. Moreover, strategic scaffolding was negatively related to problem‐solving efficiency, whereas metacognitive scaffolding positively influenced problem‐solving efficiency. Takeaways This study shows the importance of metacognitive scaffolding in improving the accuracy of metacognitive monitoring and problem‐solving efficiency. Findings from this study provide new insights for instructors and ITS developers to optimise the design of scaffoldings.
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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 | 0.001 | 0.020 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".