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Record W3044777265 · doi:10.5430/ijhe.v9n4p320

Promoting Students’ Autonomy through Online Learning Media in EFL Class

2020· article· en· W3044777265 on OpenAlexvenueno aff
Muhammad Muhammad

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldComputer Science
TopicEnglish Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsEnthusiasmAutonomyClass (philosophy)Mathematics educationPsychologyEnglish as a foreign languageQualitative researchPedagogyForeign languageSociologyComputer sciencePolitical scienceSocial psychologySocial science

Abstract

fetched live from OpenAlex

The trend of teaching English as foreign language in 21st century has changed from teacher centered to students centered paradigm which results the need of students’ autonomy in learning process. Then, this research was aimed to depict how the schoology as e-media to promote 25 VII A class students’ autonomy in learning English as foreign language (EFL) in discourse analysis course. This was a case study qualitative research by applying triangulation of the data. The results of this research mentioned that schoology successfully promoted students’ autonomy by considering some facts such as students active participations through logging in and commenting others’ idea; students’ control on deciding learning modes, setting, and materials; and students’ enthusiasm to finish the lecturer’s challenges. One unique thing was that shy students were more active in Schoology. Finally, this research suggests to present schoology in teaching content course.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.335
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 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

Citations73
Published2020
Admission routes1
Has abstractyes

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