Designing a Creative Problem-Solving-Based English Reading and Writing Curriculum for Chinese University Students
Bibliographic record
Abstract
The cultivation of 21st-century competencies in higher education demands an integrated approach that simultaneously enhances language proficiency and problem-solving capability. In the Chinese English as a Foreign Language (EFL) context, however, traditional university English instruction remains predominately exam-oriented, offering few opportunities for students to engage in authentic problem-solving tasks. This study presents a design of a creative problem-solving (CPS)-based English reading and writing curriculum for Chinese university students. Grounded in a five-step CPS teaching approach, which includes identifying problems, defining problems, finding solutions, evaluating solutions, and implementing plans, the curriculum integrates thematic units addressing real-world topics such as unemployment, career choice, and career development. The development process drew on a literature review, expert evaluation, and iterative refinement based on feedback. The resulting curriculum features critical reading activities, collaborative brainstorming, structured debates, writing workshops, and reflective practices, supported by a combination of formative and summative assessments. Findings indicate the curriculum’s potential to foster higher-order thinking, creativity, and linguistic competences, aligning with current calls for innovative, learner-centered approaches in EFL education. This work provides practical insights for curriculum developers, language educators, and higher education policymakers seeking to integrate CPS pedagogy into language learning.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".