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Record W4411781199 · doi:10.5539/elt.v18n7p62

Beyond the Module: Student Perspectives on Bloom's Taxonomy in a Longitudinal Higher Vocational English Language Course

2025· article· en· W4411781199 on OpenAlexvenueno aff
Dandan Xie, Jianzhu Liu

Bibliographic record

VenueEnglish Language Teaching · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
FundersJilin Office of Philosophy and Social Science
KeywordsPsychologyCourse (navigation)Taxonomy (biology)Mathematics educationVocational educationEnglish languageLinguisticsPedagogy

Abstract

fetched live from OpenAlex

This study investigated higher vocational college students' perceptions of their learning experience and perceived ability development within a 16-week English course designed using Bloom's Taxonomy. Employing a mixed-methods approach, data were collected from 38 students via a questionnaire comprising Likert-scale items on learning experience and perceived competence, alongside open-ended questions. Quantitative analysis revealed strong positive perceptions of the Bloom's Taxonomy-informed learning experience and high self-reported development of various competencies, with a two-factor structure identifying 'Perceived Competence' and 'Learning Activities Aligned with Bloom's Taxonomy' as distinct, yet strongly correlated, constructs. Qualitative thematic analysis further indicated that students highly valued oral communication activities, reporting significant improvements in speaking, critical thinking, analytical skills, and creative expression. While generally satisfied, students expressed a desire for increased practical application and real-world English communication opportunities. The findings suggest that integrating Bloom's Taxonomy in higher vocational English education effectively fosters perceived higher-order thinking skills and offers valuable insights for curriculum enhancement to better prepare students for professional communication demands.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.386
Teacher spread0.359 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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