MétaCan
Menu
Back to cohort
Record W2733950969 · doi:10.5539/elt.v10n8p1

Strategies Used by Professors through Virtual Educational Platforms in Face-To-Face Classes: A View from the Chamilo Platform

2017· article· en· W2733950969 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsFace-to-faceContext (archaeology)PsychologySpace (punctuation)Face (sociological concept)Qualitative researchVirtual spaceVirtual classroomMathematics educationMultimediaComputer sciencePedagogySociologyArtificial intelligence

Abstract

fetched live from OpenAlex

This study consisted in characterizing the strategies used by professors; implemented through virtual educational platforms. The context of this research were the classrooms of the Santiago de Cali University and the virtual space of the Chamilo virtual platform, where two professors from the Faculty of Education of the same university participated. The methodological approach of the investigation is qualitative, the observation and the interviews were the procedure to collect the data used. The results obtained and the analyzed literature show that there are different strategies used by professors through the implementation of the Chamilo virtual educational platform as a support to face-to-face classes, leading us to recognize that its utility has different intentions at the moment of being implemented by the professor.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.000
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 head0.032
GPT teacher head0.333
Teacher spread0.301 · 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