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Record W3200207520 · doi:10.17533/udea.ikala.v26n3a04

Spanish Adult Students’ Intention- Behaviour Toward MOOCs During the COVID-19 Pandemic

2021· article· en· W3200207520 on OpenAlexaff
María Ángeles Escobar Álvarez, Julie Ciancio

Bibliographic record

VenueÍkala Revista de Lenguaje y Cultura · 2021
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsWycliffe College
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicDistance educationPsychologyPerceptionTest (biology)Medical education2019-20 coronavirus outbreakFace (sociological concept)Higher educationMassive open online courseMathematics educationPedagogyPolitical scienceSociologyMedicine

Abstract

fetched live from OpenAlex

Many Spanish students need to learn English beyond the age of 25 to be able to find a job or be further promoted. Unfortunately, those who attempt to pass a university entry-qualifications test often lack the required academic level. To help them achieve this goal, they are usually provided with learning materials and supportive digital resources. During the covid-19 pandemic, the need for online resources increased. This is why the National Distance Education University offered a massive open online course (mooc) on elementary English. The main goal of this contrastive qualitative study was twofold: First, it attempted to explore adult students’ intention-behaviour while taking the course. Secondly it delved into students’ satisfaction with this type of courses during two different years: 2017 and 2020 when the pandemic had a clear impact on distance education. For this purpose, the study used a comprehensive post-questionnaire given at the end of both courses. The data revealed a few significant differences regarding students’ satisfaction, intentions, perceptions, and interests in contexts where face-to-face-learning was not an option. These findings suggest that mooc should be considered as an alternative way to build specific content in situations of crisis.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.029
GPT teacher head0.321
Teacher spread0.292 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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