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Record W3011496080 · doi:10.5539/ies.v13n4p75

Collaborative Learning Practices by Gender: A Case of a Community School in Nepal

2020· article· en· W3011496080 on OpenAlexvenueno aff
Kamal Prasad Acharya, Milan Acharya, Madhav Kumar Shrestha

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
FundersMinistry of Education, IndiaUniversity Grants CommissionTribhuvan University
KeywordsDiversity (politics)GirlPsychologyMathematics educationCurriculumTest (biology)Collaborative learningCooperative learningPedagogyTeaching methodSociologyDevelopmental psychology

Abstract

fetched live from OpenAlex

This study explores the situation of basic level community school boy and girl students’ science and health learning through collaborative culture. Fifth, sixth and seventh-grade students (both boys and girls) took part in this study. As per the aim of this study, a set of questionnaire was developed and administered to the sampled students. Two hundred and fifty-eight students were selected from the sampled school using the census sampling technique. All the students were involved in collaborative learning activities such as building trust and open communication, establishing group interaction, respect diversity and sharing creative ideas based on the basic level science and health curriculum aiming at promoting inquiry learning through collaboration. A quantitative analysis involving the use of the Chi-square test at 0.05 level of significance, Likelihood ratio and Somer’s symmetric was conducted to see the association between the variables. The results showed that science and health collaborative learning activities by gender were not associated significantly. There was no statistically significant difference (α=0.05) between the variables studied. The findings showed a weak association with gender and collaborative science and health learning activities in the classrooms.

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)0.018
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 score0.149
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.018
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 integrity0.0000.000
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 head0.166
GPT teacher head0.508
Teacher spread0.342 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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