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Record W1499546439 · doi:10.19173/irrodl.v14i3.1430

Virtual attendance: Analysis of an audiovisual over IP system for distance learning in the Spanish Open University (UNED)

2013· article· en· W1499546439 on OpenAlexvenueno aff
Esteban Vázquez, Javier Fombona Cadavieco, Alberto Fernández-Costales

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationAttendanceThe InternetComputer scienceVirtual campusOpen universityHigher educationMathematics educationOpen educationInformation and Communications TechnologyCitizen journalismEducational technologyMultimediaSociologyPsychologyWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

<p>This article analyzes a system of virtual attendance, called “AVIP” (AudioVisual over Internet Protocol), at the Spanish Open University (UNED) in Spain. UNED, the largest open university in Europe, is the pioneer in distance education in Spain. It currently has more than 300,000 students, 1,300 teachers, and 6,000 tutors all over the world, besides Spain. This university is redefining, on the lines of other universities, many of its academic processes to meet the new requirements of the European Higher Education Area (EHEA). Since its inception, more than 30 years ago, the methodology chosen by UNED has been blended learning. Today, this university combines face-to-face tutorial sessions with new methodological proposals, mediated by ICT. Through a quantitative methodology, the perception of students and tutors of the new model of virtual tutoring, called AVIP Classrooms, was analyzed. The results show that the new model greatly improves the orientation and teaching methodology of tutors. However, it requires training and new approaches to provide a more collaborative and participatory environment for students.</p>

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 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)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.069
GPT teacher head0.452
Teacher spread0.383 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2013
Admission routes1
Has abstractyes

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