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Record W4413773568 · doi:10.5539/hes.v15n4p13

Using Topic-based Approach to Plan a Lesson on Lexical Collocations for Saudi EFL Students

2025· article· en· W4413773568 on OpenAlexvenueno aff
Othman Aljohani

Bibliographic record

VenueHigher Education Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyLesson planTeaching methodComputer scienceLinguistics

Abstract

fetched live from OpenAlex

Habitual co-occurrence of lexical items referred to as collocations have been reported in research studies to significantly impact language learning experience by developing proficiency in both receptive and productive skills, especially in English as a Foreign Language (EFL) contexts. Lexical collocations which are formed out of the content words facilitate the acquisition of naturalness and fluency among EFL learners. Blending content analysis and topic-based approaches, this study attempted a lesson plan on lexical collocations for elementary level EFL students. The researcher used Presentation, Practice and Production (PPP) paradigm in the lesson procedure, and all lesson stages, materials, tasks and activities were built around this framework. The lesson was delivered in real-time classroom setting and the researcher took notes of the main events which were then reflected upon for a post-lesson analysis of the teacher performance. While most of the stages, tasks and activities were delivered according to the plan and had a satisfactory level of student participation, a few of the lesson activities could not be administered effectively due to time management issues. Nevertheless, the study is expected to provide some useful insights to EFL practitioners about developing the lexical range of elementary level learners in general and collocational competence in particular. It is also anticipated that the article might also help other scholars to conduct further research on the topic.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.898

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 communication0.0000.000
Open science0.0000.000
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 head0.205
GPT teacher head0.517
Teacher spread0.312 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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