An Optimisation Approach to Promoting Students’ Critical Thinking Skills by Incorporating English Civics Elements into the EFL Classroom Based on the Coefficient of Variation Method and Neural Network Algorithm
Bibliographic record
Abstract
The integration of Civics elements into the EFL classroom is an organic supplement and deepening of the teaching content and materials, while EFL Civics classroom teaching is a powerful means to strengthen the deep and longitudinal development of students’ critical thinking. This paper discusses the relationship between EFL Civics classroom teaching and the development of critical thinking ability from the theoretical and practical levels respectively. On the basis of existing research, the evaluation index of students’ critical thinking ability is proposed. The CVM coefficient of variation method is improved, and the ICVM and BP neural network algorithm are combined to constitute the evaluation model of students’ critical thinking ability based on ICVM and BP neural network. According to the evaluation process, the level of students’ critical thinking ability after EFL-based Civics classroom teaching is derived. It also integrates teachers’ and students’ evaluation of the effect of English Civics elements integrated into the EFL classroom, and finally obtains the practical teaching effect of the EFL Civics classroom. The overall mean value in the teacher’s side is greater than 3.5 points, which indicates that teachers are basically positive about the effect of integrating Civics in EFL courses, and basically agree with the positive impact of English Civics elements on EFL classroom teaching. Based on the evaluation results of ICVM and BP model, the evaluation scores of students’ critical thinking skills and critical thinking monitoring are higher than the evaluation scores of critical thinking tendency, i.e., the elements of English Thinking can be effectively integrated into the EFL classroom and promote the development of students’ critical thinking skills.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".