Using learning automata to model the “learning process” of the teacher in a tutorial-like system
Bibliographic record
Abstract
Unlike the field of Tutorial systems, where a real-life student interacts and learns from a software system, our research focuses on a new philosophy in which no entity need be a real-life individual. Such systems are termed as Tutorial-like systems, and research in this field endeavours to model every component of the system using an appropriate learning model (in our case, a Learning Automaton (LA)). While models for the Student, the Domain, the Teacher, etc. have been presented elsewhere, the aim of this paper is to present a new approach to model how the Teacher, in this paradigm, “learns” and improves his “teaching skills” while being himself an integral component of the system. We1 propose to model the “learning process” of the Teacher by using a higher level LA, referred to as the Meta-Teacher, whose task is to assist the Teacher himself. Ultimately, the intention is that the latter can communicate the teaching material to the Student(s) in a manner customized to the particular Student’s ability and progress. In short, the Teacher will infer the progress of the Student, and initiate a strategy by which he can “customcommunicate” the material to each individual Student.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".