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Record W2108113345 · doi:10.5539/elt.v7n8p62

Graduate Student End-of-Term Satisfaction with Group-Based Learning in EFL Classroom

2014· article· en· W2108113345 on OpenAlexvenueno aff
Wen Li, Shoukuan Mu

Bibliographic record

VenueEnglish Language Teaching · 2014
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyClass (philosophy)Mathematics educationGraduate studentsEnglish as a foreign languageFocus groupTerm (time)PedagogyMedical educationComputer science

Abstract

fetched live from OpenAlex

The current study explored graduate student end-of-term satisfaction with group learning, compared with traditional instructor-led instruction in EFL (English as a foreign language) classroom. Participants were 74 graduate students, including 33 males and 41 females from a normal university in southern China. The study was carried out with two classes by different teaching methodologies respectively, one was group-based (n/35) with nine groups, and the other was instructor-led class (n/39). Students were assigned randomly to the two types of classes ahead of the formal lessons and taught by the same instructor during the period of an academic term. At the end of the term, a questionnaire survey was administered to all the students of the two classes to measure their satisfaction with English class learning. The results showed students with group-based instruction were more satisfied than those who took the course under the instructor-led format. Also, no significant differences existed between groups with respect to satisfaction. The results of the analysis were discussed and directions for further study were suggested. The significance of the present study lies in the fact that it was able to explore the difference in student satisfaction between group-learning and instructor-led settings in EFL class, and both instructor(s) and students should shift their focus “from what is being taught to what is being learned” in EFL classroom.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 science0.0000.000
Research integrity0.0000.002
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 head0.024
GPT teacher head0.351
Teacher spread0.327 · 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
Published2014
Admission routes1
Has abstractyes

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