A New Filtering Model Towards An Intelligent Guide Agent
Bibliographic record
Abstract
In E-learning systems, where both helpers (tutors) and learners are separated geographically, finding a reliable helper is one of the most important challenges. Al-though helpers could have a lot of useful information about courses to be taught, many learners fail to un-derstand their presentations. A major part of this paper deals with the following challenges: do helpers have in-formation that the learners need? Will helpers present information so that learners can understand? And can we guarantee that these helpers will collaborate effec-tively with learners? A new technique is filtering ac-cording to helpers ’ credibilities. We define ”credibil-ity ” as the dependability degree of the learners on the information presented by helpers during a learning ses-sion. We propose a guide agent, based on the pyramid model, which can group helpers. This makes it possible to recommend reliable ones. Furthermore, we devel-oped a new statistical metric called Precision Probabil-ity Value. We have used this metric to measure statisti-cal accuracies rather than the mean absolute error.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".