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Record W7055196496

CASE STUDY OF THE TOEFL IBT PREPARATION COURSE

2007· article· en· W7055196496 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2007
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTest of English as a Foreign LanguageTest (biology)Teaching methodData collectionTest preparationCourse (navigation)Qualitative researchNeeds analysis
DOInot available

Abstract

fetched live from OpenAlex

On account of the introduction of the Next Generation TOEFL (Test of English as a Foreign Language Internet-based Test) in September 2005, it is worthwhile to explore the impact of the new test on ESL/EFL teaching and learning. The purpose of this study was to investigate the TOEFL iBT preparation course from both teachers’ and students’ perspectives. This study was conducted in a Southwestern Ontario town from January to April 2007. In total, six teachers and four students were involved in this study. A qualitative case study method was used in this research. Personal interviews and classroom observations were used as the main tools to collect data. Survey and document analysis were employed as supplementary instruments. A constant comparative method (Merriam, 1998) was used to conduct data analysis. Findings from this study reveal that participants have positive attitudes toward the change and the new test has had an influence on their teaching and learning practices to various degrees. Based on the findings, this study makes suggestions for teachers, students, and test designers respectively. Teachers should try to break the general skills into teachable elements and should be more aware of cross-cultural learning issues. Students should explore opportunities to speak English and prepare themselves for the possibly noisy test environment. Test designers should provide informative workshops to teachers on how to evaluate the performance of their students by using the rubrics.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.081
GPT teacher head0.318
Teacher spread0.237 · 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 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

Citations0
Published2007
Admission routes1
Has abstractyes

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