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Record W4404823124 · doi:10.5539/elt.v17n12p77

The Development of Self-directed Learning through Blended-Learning Approach for Students in English Language Subject Group: The Opportunity Expansion Schools

2024· article· en· W4404823124 on OpenAlexvenueno aff
Ammaret Netasit, Busarakham Intasuk, Panotnon Teanprapakun, Pongwat Fongkanta, Fisik Sean Buakanok

Bibliographic record

VenueEnglish Language Teaching · 2024
Typearticle
Languageen
FieldComputer Science
TopicHigher Education and Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySubject (documents)Mathematics educationBlended learningGroup (periodic table)Language acquisitionPedagogyEducational technologyComputer scienceLibrary science

Abstract

fetched live from OpenAlex

Self-directed learning and blended learning are instructional methods to enhance students’ current learning behavior and promote their life-long 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 effects of teaching approach. 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’s evaluation form in teacher’s self-directed learning management behavior; and a students’ evaluation form for students’ self-directed learning behavior. The data analysis employed percentage, mean, standard deviation, and dependent t-test. This study found two principal outcomes: 1) the teacher’s average mean score on the instructional behavior was higher than before the teaching intervention was implemented, with statistical significance of 0.05; and 2) the students’ average mean score on self-directed learning behavior was higher than before they engaged in the self-directed learning programme, with the statistical significance of 0.05.

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)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.069
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.023
GPT teacher head0.339
Teacher spread0.316 · 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 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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