Developing College Students’ Entrepreneurial Competences: Designing Project-Based Learning Entrepreneurship Foundational Course in Chinese Universities
Bibliographic record
Abstract
This study introduces the design process of a Project-Based Learning (PBL) entrepreneurship foundational course for Chinese universities. To address deficiencies in the current entrepreneurship foundational courses in Chinese universities and to enhance the overall entrepreneurial competence of college students, project-based learning has integrated into the entrepreneurship foundation course. Based on the project-based learning framework proposed by Han and Bhattacharya (2001), a project-based learning entrepreneurship foundational course has been developed for Chinese universities by utilising expert panels. Once the course design was completed, a semester-long project-based learning entrepreneurship foundational course was implemented. We measured the effectiveness of the course through student feedback. The course objectives were to develop student opportunity competence, business management competence, and interpersonal competence in entrepreneurship. This paper presents the course outline, teaching unit design, and teaching activity design. In addition, this study explored the relevant factors that should be considered in the development of any project-based learning entrepreneurship foundational course. This study developed the entrepreneurial competence of college students through the design of a project-based learning entrepreneurship foundational course and implemented a one-semester course in a university in China. The results of this study can serve as a guide for universities who wish to implement project-based learning entrepreneurship foundational courses, which can help improve the entrepreneurial competence of college students.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".