An Answering System for Questions Asked by Students in an e-Learning Context
Bibliographic record
Abstract
En aquest article presentem un sistema que ajuda els docents a respondre les preguntes dels seus alumnes en una universitat virtual, concretament la Universitat Oberta de Catalunya (UOC). La comunicació entre alumne i docent es fa d'una manera totalment virtual: les preguntes i les respostes es formulen i contesten per correu electrònic. El sistema, que es va desenvolupant a l'Àrea de Tecnologia Educativa de la UOC, té com a principal objectiu trobar contextos multilingües amb informació útil per a respondre a l'estudiant d'una manera ràpida i adequada. Els contextos s'extreuen dels materials del curs, els fòrums de participació de l'assignatura, articles i altres fonts d'informació disponibles a internet. A més d'ajudar els docents a trobar millors respostes, el sistema és útil per a actualitzar els seus coneixements i desenvolupar el seu aprenentatge permanent.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".