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Record W4411882499 · doi:10.5539/jel.v14n6p222

Self-Directed Learning Development through Blended Learning in English Opportunity Expansion Schools

2025· article· en· W4411882499 on OpenAlexvenueno aff
Ammaret Netasit, Busarakham Intasuk, Panotnon Teanprapakun, Pongwat Fongkanta, Fisik Sean Buakanok

Bibliographic record

VenueJournal of Education and Learning · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
FundersResearch and Innovation Foundation
KeywordsBlended learningMathematics educationPsychologyPedagogyEducational technology

Abstract

fetched live from OpenAlex

Self-directed learning and blended learning are instructional methods to enhance students’ current learning behaviour and promote their lifelong learning. This research aims to develop the self-directed learning instructional model by implementing a blended learning method with English subject group teachers in opportunity expansion schools in Lampang province, Thailand, and to examine the model’s effects. Thirty English language teachers were selected as the sample group by using the voluntary sampling technique. The research instruments were an interview form, a lesson plan assessment form, a teacher evaluation form about teachers’ self-directed learning management behaviour, and a student evaluation form about students’ self-directed learning behaviour. The data analysis employed percentage, mean, standard deviation, and dependent t-test. This study had two principal outcomes: (1) teachers’ average mean score on instructional behaviour was higher than before the teaching intervention was implemented, a with statistical significance of 0.05; and (2) students’ average mean score on self-directed learning behaviour was higher than before they engaged in the self-directed learning programme, with a statistical significance of 0.05. These results highlighted that to empower students’ learning abilities, it is important to emphasise goal setting, hands-on learning, and teachers’ role in coaching.

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)0.004
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
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 head0.031
GPT teacher head0.373
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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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