Developing an Educational Practice Course through Outcome-Based Education: Enhancing Practical Ability of Pre-Service Teachers in Guangxi, China
Bibliographic record
Abstract
This study investigates the development and effectiveness of an educational practice course grounded in Outcome-Based Education (OBE) principles, aiming to enhance the practical competencies of pre-service Chinese language teachers at Guangxi Minzu Normal University. Adopting a backward design framework, the course was structured around six core areas of teaching ability: classroom teaching, curriculum design, classroom management, psychological counseling, educational research, and teaching reflection. A total of 40 pre-service teachers participated in a 13-week structured internship program combining university guidance with school-based mentoring. Quantitative data were collected through two researcher-developed instruments: the Chinese Classroom Teaching Self-Evaluation Scale and the Educational Practice Ability Evaluation Scale. Pre- and post-test analyses revealed statistically significant improvements across all competency domains, with the most notable growth observed in psychological counseling (+52%) and curriculum design (+43%). Qualitative data from reflective journals and mentor feedback further confirmed the program’s impact on teaching readiness and professional identity development. The findings underscore the value of structured, outcome-driven practicum models in bridging the gap between theory and practice. The study concludes by recommending enhanced mentorship systems, standardized evaluation frameworks, and continued research into OBE applications in diverse educational contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".