A framework for identifying and promoting metacognitive knowledge and control in online discussants
Bibliographic record
Abstract
The effectiveness of computer-based learning environments depends on learners’ deployment of metacognitive and self-regulatory processes. Analysis of transmitted messages in a context of Computer Mediated Communication can provide a source of information on metacognitive activity. However, existing models or frameworks (e.g., Henri, 1992) that support the identification and assessment of metacognition have been described as subjective, lacking in clear criteria, and unreliable in contexts of scoring. This paper develops a framework that might be used by researchers analysing transcripts of discussions for evidence of engagement in metacognition, by instructors assessing learners’ participation in online discussions or by designers setting up metacognitive experiences for learners. Résumé : L’efficacité des environnements d’apprentissage assistés par ordinateur repose sur l’utilisation de processus de métacognition et d’autorégulation par les apprenants. L’analyse de messages transmis dans un contexte de communication assistée par ordinateur peut constituer une source d’information sur l’activité métacognitive. Cependant, les modèles et cadres existants (p. ex. Henri, 1992) qui permettent la reconnaissance et l’évaluation de la métacognition ont été décrits comme subjectifs, dépourvus de critères clairs et peu fiables dans des contextes de notation. Cet article décrit un cadre qui pourrait être utilisé par les chercheurs qui analysent les transcriptions de discussions à la recherche de preuves d’engagement métacognitif, par les instructeurs qui procèdent à l’évaluation de la participation des apprenants à des discussions en ligne ou par les concepteurs qui élaborent des expériences métacognitives pour les apprenants.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".