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Record W3160586265 · doi:10.5539/ijel.v11n3p96

Learning in Higher Education Under the Covid-19 Pandemic: Were Students More Engaged or Less?

2021· article· en· W3160586265 on OpenAlexvenueno aff
Evelyn Eika

Bibliographic record

VenueInternational Journal of English Linguistics · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyBlended learningAttendanceExperiential learningHigher educationLearning stylesActive learning (machine learning)PandemicEducational technologyMedical educationPedagogyMathematics educationCoronavirus disease 2019 (COVID-19)MedicinePolitical science

Abstract

fetched live from OpenAlex

This study explored students’ learning experiences in higher education during the Covid-19 pandemic. A journal writing methodology was used to extract learners’ reflective thoughts regarding their living and learning during the pandemic outbreak. The results were interpreted through the views of relevant student engagement frameworks. The students’ structural factors (family, support, and pressure) were impacted because of political and sociocultural factors (restrictive measures in response to the pandemic outbreak) within which the university factors were embedded (total closure with online education, subsequent reopening allowing physical attendance, and later principal distance education with approved exceptions), which collectively and psychosocially influenced students’ life and studies. The learners self-adapted via their individual efficacy to tackle the unfamiliar situations by digitally reaching out to family/friends and enhancing skills/self-learning; learner differences in learning style and preferences were noted. Online courses offered flexibility for learning independent of time and space while social presence in the learning community during online lessons remained less effective; traditional values of face-to-face physical classrooms were recognised among some learners. Learners’ perceived effective engaging measures underscored the importance of ensuring learner well-being (counselling and mask-wearing), learning independence (online lecture recordings and optional attendance), and strengthening online learning experiences (building the learning community, enhancing class dialogue, and demonstrating problem-solving techniques). Recommendations for engaging learning were discussed.

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)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.177
GPT teacher head0.492
Teacher spread0.315 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicCOVID-19 and Mental HealthFrench-language works237,207