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Record W4400920744 · doi:10.5539/hes.v14n3p104

Implementation of Cooperative Learning Method to Enhance the Students' Learning Ability and Students' Core Competencies

2024· article· en· W4400920744 on OpenAlexvenueno aff
Siyong Tang, Prasert Ruannakarn

Bibliographic record

VenueHigher Education Studies · 2024
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleMathematics educationCooperative learningTeaching methodCore competencyTeaching and learning centerPsychologyCore (optical fiber)Computer scienceManagement

Abstract

fetched live from OpenAlex

Modern society is complex and ever-changing. To adapt to this situation, the level of education must be continuously enhanced, and college students who are about to enter society are the group that needs the most attention. As a creative and effective teaching organizational form and teaching strategy, the cooperative learning method plays an important role in education and teaching. This article research aims to achieve the following two goals: 1) Compare students' learning abilities after the cooperative learning method and traditional teaching method. 2) Compare students’ core competencies after the cooperative learning method and traditional teaching method. The participants in this study were students studying physics at Guangxi Normal University for Nationalities in China. It includes a control group consisting of 30 students and an experimental group consisting of 30 students. The research tool used a Likert scale question, and the data were analyzed using normal distribution and standard deviation. The research results show that students' learning ability and core competencies after the cooperative learning method are better than traditional teaching method (P<0.01).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.147
GPT teacher head0.586
Teacher spread0.439 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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