A Multi-Agent Architecture for Peer-Help in a University Course*
Bibliographic record
Abstract
I-Help is an integration of previously developed ARIES Lab tools for peer help to university teaching. In this paper we discuss an approach to distributing the centralised monolithic architecture of I-Help, by using a multi agent-architecture. The I-Help project I-Help is an integration of previously developed ARIES Lab tools for peer help to university teaching. One of its components, CPR provides a subject-oriented discussion forum and moderated FAQ-list supporting students with electronic help. Another component, PHelpS selects an appropriate peer helper who will support the student with direct peer help via an elaborate chat-environment. The selection of appropriate discussion forum, FAQ-article or human peer helper is based on modelling learner knowledge in the context of the concept / topic structure of the subject material. A full paper describing the I-Help Project will be presented at ITS'98 (Greer et al., 1998). In this paper we discuss an approach to distributing the ...
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".