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Record W4391219576 · doi:10.5539/ies.v17n1p34

Evaluating Dual Language Education Programs in Taiwan: Structure, Instruction, and Learning Outcomes

2024· article· en· W4391219576 on OpenAlexvenueno aff
Cheng-Ji Lai

Bibliographic record

VenueInternational Education Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationDual languageNative-language instructionLanguage proficiencyTeaching methodPsychologyIndividualized instructionPedagogyComputer scienceVocabulary development

Abstract

fetched live from OpenAlex

Despite the increasing popularity of dual language education programs in Taiwan, limited research assesses their effectiveness. This study evaluated eight English Immersion Programs (EIPs) in Taiwan, representing a dual language education model, using the Guiding Principles for Dual Language Education (GPDLE) framework. Interviews with the management team and a questionnaire for Native English Teachers (NETs) assessed the alignment of program structure and instruction with the GPDLE. A quasi-experimental design, including pre-tests and post-tests, examined the English listening and reading outcomes of 74 fourth to sixth-grade students in two randomly-selected EIPs over a year. The findings reveal that 83% of the EIPs fully adhered to the program structure outlined in the GPDLE, while only 33% of teacher instruction exhibited full alignment, with an additional 57% demonstrating partial alignment. Notably, significant improvements in reading were observed among fourth-grade students, and both reading and listening skills showed substantial enhancements in the fifth and sixth-grade students. The study recommends adopting a financially sustainable, user-paid model for an after-school English immersion program, supporting Taiwan’s 2030 Bilingual National Initiative.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.090
GPT teacher head0.410
Teacher spread0.321 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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