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Record W4297349511 · doi:10.5539/elt.v15n10p75

ESP Textbook Evaluation: The Case of Kuwaiti Students of Business Administration

2022· article· en· W4297349511 on OpenAlexvenueno aff
Seham Al-Abdullah

Bibliographic record

VenueEnglish Language Teaching · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistSubject matterPsychologyCurriculumQuality (philosophy)Business EnglishEnglish for specific purposesTeaching methodMathematics educationMedical educationEnglish languagePedagogyMedicine

Abstract

fetched live from OpenAlex

Textbooks are considered an essential component of classroom materials. Teachers usually examine a wide range of textbooks offered in the book market to determine an appropriate one that fits the pedagogical demands. Textbooks’ evaluation is, therefore, important to determine and improve their suitability for students’ needs. Specifically, in teaching English for Specific Purposes (ESP), textbooks play a significant role in enhancing students’ learning of specialized English skills and using such skills to address occupational needs.  This study was undertaken to evaluate the quality of the Business Result Second Edition (2017) textbook and its suitability for the needs of students majoring in Business Administration (BA) at the College of Business Studies (CBS) in Kuwait. An evaluation checklist was utilized to examine five factors (subject matter, linguistic issues, exercises and activities, course objectives, and layout of materials and visuals). Participants included 10 English language teachers who taught the ESP course at CBS. Results indicated that despite having some disadvantages, the textbook was relatively suitable for the course. The findings of this study can be adopted by the curricula designers at CBS to improve or modify the textbook in question.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.289
Teacher spread0.266 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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