Modeling a Student's Behavior in a Tutorial-<i>Like</i> System Using Learning Automata
Bibliographic record
Abstract
This paper presents a new philosophy to model the behavior of a student in a tutorial- like system using learning automata (LAs). The model of the student in our system is inferred using a higher level LA, referred to as a meta-LA , which attempts to characterize the learning model of the students (or student simulators), while the latter use the tutorial-like system. The meta-LA , in turn, uses LAs as a learning mechanism to try to determine if the student in question is a fast, normal, or slow learner. The ultimate long-term goal of the exercise is the following: if the tutorial- like system can understand how the student perceives and processes knowledge, it will be able to customize the way by which it communicates the knowledge to the student to attain an optimal teaching strategy. The proposed meta-LA scheme has been tested for numerous environments, including the established benchmarks, and the results obtained are remarkable. Indeed, to the best of our knowledge, this is the first published result that infers the learning model of an LA when it is externally treated as a black box, whose outputs are the only observable quantities. Additionally, our paper represents a new class of multiautomata systems, where the meta-LA synchronously communicates with the students, also modeled using LAs. The meta-LA's environment "observes" the progress of the student LA, and the response of the latter to the meta-LA actions is based on these observations. This paper also discusses the learning system implications of such a meta-LA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".