Computational Aspects of the Intelligent Tutoring System MetaTutor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present in this chapter the architecture of the intelligent tutoring system MetaTutor that trains students to use metacognitive strategies while learning about complex science topics. The emphasis of this chapter is on the natural language components. In particular, we present in detail the natural language input assessment component used to detect students’ mental models during prior knowledge activation, a metacognitive strategy, and the micro-dialogue component used during sub-goal generation, another metacognitive strategy in MetaTutor. Sub-goal generation involves sub-goal assessment and feedback provided by the system. For mental model detection from prior knowledge activation paragraphs, we have experimented with three benchmark methods and six machine learning algorithms. Bayes Nets, in combination with a word-weighting method, provided the best accuracy (76.31%) and best human-computer agreement scores (kappa=0.63). For sub-goal assessment and feedback, a taxonomy-driven micro-dialogue mechanism yields very good to excellent human-computer agreement scores for sub-goal assessment (average kappa=0.77).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it