Developing a Chinese Language Course Integrating Deep Learning Theory and OBE: Promote Critical Thinking Skill for Undergraduate Students in Guangzhou, China
Bibliographic record
Abstract
This study aims to develop and evaluate a Chinese language course designed to enhance college students' critical thinking skills through the integration of deep learning theory and Outcome-Based Education (OBE). The research specifically addresses two questions: (1) What are the characteristics of a Chinese language course that integrates Deep Learning Theory and OBE? (2) What are the effects of this course on promoting critical thinking skills among undergraduate students? The study employs a quasi-experimental design, involving 120 undergraduate students divided into a control group and a test group from Nan fang College, Guangzhou, China. The control group received conventional teaching methods, while the test group participated in the newly developed course. Data were collected through pre-tests and post-tests using California critical thinking skills test (Chinese version), as well as semi-structured interviews. Results indicate a significant improvement in the critical thinking skills of students in the test group compared to the control group. The test group showed higher mean scores and lower standard deviations in post-test results, demonstrating the effectiveness of the course in enhancing critical thinking abilities. Qualitative data from interviews supported these findings, highlighting increased student engagement and deeper understanding of course materials. The findings suggest that this integrated approach can be effectively implemented in other educational contexts to achieve similar outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".