Effects of Mind Map Integrated Project-Based Learning on the Reduction of English Speaking Anxiety on Chinese Undergraduates
Bibliographic record
Abstract
The problems of English speaking unproficiency, low interest and silent English classrooms among students in Chinese higher vocational colleges have become an issue in recent years in China. Due to the importance of the mind map has become very popular in English language instruction, this paper carries out a study to investigate the effectiveness of Mind Map integrated Project-Based Learning (PjBL) strategy in public English language teaching in one of the Chinese higher vocational colleges and designs an English speaking project on food in the campus cafeteria. Twenty freshman students majoring in preschool education are selected in the experimental group and divided into four sub-groups of five students per group to select their tasks for the project so that they can be stimulated to finish the project independently and cooperatively. The other twenty students of the same major are in the control group to compare. The study uses mixed methods by combining quantitative and qualitative methods through students’ English speaking anxiety tests before and after the project by using Horwitz’s Foreign Language Classroom Anxiety Scale (FLCAS) and a semi-structured interview as a supplement to investigate. It turns out that the Mind Map Integrated Project-Based Learning (PjBL) Strategy reduced students' English speaking anxiety to a certain degree.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".