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Record W4396869229 · doi:10.5430/jct.v13n2p146

Developing Blended Learning Frameworks for High Schools: A Case Study in Nam Dinh Province, Vietnam

2024· article· en· W4396869229 on OpenAlexvenueno aff
Huy-Hoang Tran, Thi Bich Dao Pham, Loi Phan Thi Bich, Dung Nguyen Le Van

Bibliographic record

VenueJournal of Curriculum and Teaching · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsBlended learningMathematics educationGeographyPedagogyEducational technologySociologyPsychology

Abstract

fetched live from OpenAlex

This study utilized a combination of reality surveying and pedagogical experimentation to explore the application of blended learning (BL) in the educational sector of Nam Dinh province, Vietnam. The objective was to develop a BL framework tailored for the tenth-grade curriculum in Literature, Chemistry, and Mathematics. This framework was then operationalized through experimental lesson plans, which were implemented in five high schools across Nam Dinh. Results from this study confirm the effectiveness of the BL framework, suggesting its potential for wider adoption within the province. The findings contribute significantly to the enhancement of research and teaching methodologies in high schools, aligning with the 2018 national curriculum for high school students in Nam Dinh. Furthermore, this research provides valuable insights into the broader educational context of Vietnam.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.353
Teacher spread0.336 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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