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Record W2591764994 · doi:10.5539/elt.v10n4p22

Teaching “Cross-cultural Communication” through Content Based Instruction: Curriculum Design and Learning Outcome from EFL Learners’ Perspectives

2017· article· en· W2591764994 on OpenAlexvenueno aff
Chia-Ti Heather Tseng

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCurriculumVariety (cybernetics)Mathematics educationCritical thinkingTeaching methodLanguage acquisitionCooperative learningPedagogyComputer science

Abstract

fetched live from OpenAlex

This study aims to investigate EFL learners’ perspectives for the effectiveness of content-based instruction in a cross-cultural communication course. The main objectives of this study are three-folds: (1) to examine students’ perspectives regarding the effectiveness of content learning; (2) to examine students’ perspectives regarding the effectiveness of language learning; and (3) to examine students’ perspectives regarding the effectiveness of cooperative learning and development of critical thinking. Sixty non-English major EFL students from a university in Northern Taiwan participated in this study. A variety of tasks such as readings of a variety of authentic texts, viewing movie and video clips, discussing in groups, and accomplishing a group project were employed to have students actively explore the subject content and concurrently work on their language skills. Students were also required to evaluate their peers’ final group project with provided evaluation criteria. Questionnaires and semi-structured interviews were conducted to explore what students were able to learn from this course and the challenges they have encountered. The results from students’ feedback revealed their positive gains in the areas of content knowledge as well as the enhanced language skills. Some perceived difficulties among students such as inability to fully comprehend the input or to produce effective output were reported and the pedagogical solutions were suggested. Other benefits such as constructive cooperative learning, enhanced critical thinking, and boosted confidence in the target language use were also reported by the learners.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.312
Teacher spread0.260 · 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 designObservational
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

Citations20
Published2017
Admission routes1
Has abstractyes

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