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Record W2809138517 · doi:10.5539/elt.v11n7p131

Innovative Scenarios in the Teaching and Learning Process: A View From the Implementation of Virtual Platforms

2018· article· en· W2809138517 on OpenAlexvenueno aff
Heriberto González Valencia, Jakeline Amparo Villota Enríquez, María Eufemia Freire Tigreros

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsBlackboard (design pattern)Process (computing)Asynchronous communicationComputer scienceVirtual learning environmentClass (philosophy)Teaching methodAction researchMultimediaEducational technologyMathematics educationHuman–computer interactionPsychologySoftware engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this research article an analysis and evaluation of virtual learning spaces is carried out. To address this process, three (3) academic virtual platforms were selected, Moodle, Blackboard and Jimdo. The methodology used was of a descriptive type, applying the synchronous and asynchronous methods of virtual teaching established in each of the Institutions. A description and evaluation of each virtual platform was carried out taking into account four identified criteria: the technical and material design; instructional design; tutorial action and the virtual class. The evaluation using educational platforms generated scenarios of innovation from the social view since it allowed both the teacher and student to rethink the evaluation not only as an instrument but as a transversal process linked to different fields such as: social, cultural, economic, etc.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.006
Scholarly communication0.0080.006
Open science0.0010.006
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.310
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2018
Admission routes1
Has abstractyes

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