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Record W4383907971 · doi:10.5430/wjel.v13n7p89

Exploring the Effects of Concept Mapping on Undergraduate Students’ EFL Reading Comprehension in China

2023· article· en· W4383907971 on OpenAlexvenueno aff
Na Ta, Abu Bakar Razali, Fazilah Razali

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsnot available
Fundersnot available
KeywordsSyllabusReading comprehensionReading (process)Mathematics educationConcept mapCurriculumComprehensionComputer scienceChinaPsychologyPedagogyLinguistics

Abstract

fetched live from OpenAlex

Reading is a fundamental life-long learning tool to achieve personal and professional success, and it has been attached with great importance in China. However, despite the significance of reading comprehension, EFL undergraduate students in China face challenges when reading in English language. It is essential for both teachers and researchers to explore effective teaching techniques to teach reading. In this regard, concept mapping can serve as a specific form of teaching and learning technique for reading instruction. The present study employed a quasi-experiment to investigate the effects of concept mapping on the EFL reading comprehension of undergraduate students in China. The study spanned 32 instructional sessions, and the effects on knowledge retention were measured two weeks after the experiment. The participants included 135 second-year undergraduate students from three intact EFL classes at a university in China. The findings revealed that the use of concept mapping technique was effective in improving students’ reading comprehension. Moreover, the combined use of concept mapping with conventional method was more effective than fully using concept mapping in promoting students’ reading comprehension and knowledge retention. The study gives pedagogical and policy implications for teachers, syllabus designers, curriculum developers, and other stakeholders.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.056
GPT teacher head0.333
Teacher spread0.277 · 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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