A Metamodel for Designing an Intelligent Tutoring Systems Authoring Tool
Bibliographic record
Abstract
Previous intelligent tutoring systems (ITS) and ITS authoring studies predominantly simulated and evaluated artificial intelligence (AI) techniques and cognitive architectures/notions in educational domains. Current research focuses on software design that is priori driven by educational theories; it concerns the conception of Augmented Conversation and Cognitive Apprenticeship Metamodel (ACCAM). The pedagogy driven metamodel?ACCAM?forms the basis for a formal (theory based) approach to designing ITS authoring tools for numerical aspect of numerical disciplines. This research, therefore, showcases the convergence of two theoretical perspectives—the Conversation Theory (CT) and Cognitive Apprenticeship (CA)—which were never considered together before now. The novel conceptual platform?the ACCAM—flows and benefited from the synergistic effect of the stated theories through the introduction of the concept of ‘augmented conversation’ within the resulting integrated framework. Thus, current work draws on the pedagogical import of the mentioned educational theories, elicits new meanings, and lays the foundation as well as opens future evaluation of a pedagogical engineering methodology that flows therefrom.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".