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Record W1987634687 · doi:10.7238/rusc.v9i2.1161

An Answering System for Questions Asked by Students in an e-Learning Context

2012· article· en· W1987634687 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRUSC Universities and Knowledge Society Journal · 2012
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsAluminium Refining, Degassing and Filtering (Canada)
Fundersnot available
KeywordsHumanitiesContext (archaeology)ArtGeography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.385
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it