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Record W4386419937 · doi:10.5430/wjel.v13n7p555

A Comparative Study between the Use of Adjectives and Adjective Clauses Based on Bloom’s Taxonomy in Writing Sentence Levels Versus Paragraph Levels

2023· article· en· W4386419937 on OpenAlexvenueno aff
Itithaz Jama

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsAdjectiveParagraphSentenceLinguisticsComputer scienceHierarchyComprehensionTaxonomy (biology)Natural language processingPsychologyArtificial intelligenceNounPhilosophy

Abstract

fetched live from OpenAlex

The purpose of this paper was to examine English as a Foreign Language (EFL) students’ comprehension of adjectives and adjective clauses by writing in different phases. The first phase, students were writing an individual sentence by using adjectives and adjectives clauses. The next phase was creating paragraphs using the same rules that they used previously. The framework of the present research was Bloom’s taxonomy to examine students’ development from the lowest levels of cognition into the highest levels of the hierarchy. The design of this study was descriptive qualitative research focusing on 20 students’ writing who used the target grammatical structures. To analyze the collected data, the author used themes to divide the data based on a sentence level and a paragraph level. The results of the research revealed EFL students’ success in applying the target grammatical rules at the lowest cognitive levels by composing an individual sentence. However, the findings showed that only six students out of 20 were able to reach the highest levels of cognitive to create paragraphs using adjective clauses; whereas, the rest of the students failed to create paragraphs using adjective clauses. However, all students successfully composed simple and accurate individual sentences using both one adjective and the other for using adjective clauses, which is considered the lowest levels of the hierarchy.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.254
GPT teacher head0.424
Teacher spread0.170 · 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
Published2023
Admission routes1
Has abstractyes

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