Commentary on: “Toward Computer-Based Support of MetaCognitive Skills: a Computational Framework to Coach Self Explanation”
Bibliographic record
Abstract
This paper is a commentary on “Toward Computer-Based Support of Meta-Cognitive Skills: a Computational Framework to Coach Self-Explanation”, by Cristina Conati and Kurt Vanlehn, published in the IJAED in 2000 (Conati and VanLehn 2010). This work was one of the first examples of Intelligent Learning Environments (ILE) that target activities beyond problem solving doing so by providing adaptive support to apply relevant domain independent meta-cognitive skills, as opposed to targeting domain-dependent knowledge. We provide an overview of the SE-Coach, the ILE presented in (Conati and Vanlehn 2000), in terms of underlying motivation, objectives, appeach and contibutions. This overview is followed by a discussion of subsequent developments in research on providing intelligent, student-adaptive support to meta-cognition, as well as of remaining open issues and avenues for future research.
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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 | 0.006 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.085 | 0.085 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".