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Record W2086122290 · doi:10.5539/elt.v6n5p141

The Effects of Blended Learning on the Intrinsic Motivation of Thai EFL Students

2013· article· en· W2086122290 on OpenAlexvenueno aff
Usaporn Sucaromana

Bibliographic record

VenueEnglish Language Teaching · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsBlended learningPsychologyMathematics educationBachelorClass (philosophy)Cooperative learningSubject (documents)Face-to-faceEducational technologyPedagogyTeaching methodComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The aim of this study is to compare the results of blended learning with face-to-face learning among university students studying English as a foreign language. The participants were separated by gender, and the following variables, intrinsic motivation for learning English, attitudes towards English as a subject, and satisfaction with the learning climate, which was either a blended learning environment or a face-to-face learning environment, were analysed. The participants of this research are bachelor’s degree students enrolled in English courses. The two class samples were drawn at random. The first class was the control group, and the other was the experimental group. The experimental group was taught using blended learning, and the control group was taught using face-to-face learning. The results of the research did not differ by gender. The students had significantly higher levels of intrinsic motivation for learning English, a better attitude towards English as a subject, and better satisfaction with the learning climate after they were taught by blended learning. Finally, the students who were taught using blended learning had significantly higher levels of intrinsic motivation for learning English and a better attitude towards English as a subject, as well as greater satisfaction with the learning climate than the students who were taught using face-to-face learning.

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)0.002
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.283
Teacher spread0.277 · 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 designQualitative
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

Citations26
Published2013
Admission routes1
Has abstractyes

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