Exploring the Effects of Concept Mapping on Undergraduate Students’ EFL Reading Comprehension in China
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".