Impact of Entrepreneurship Knowledge Literacy Curriculum on College Graduates’ Sustainable Entrepreneurial Competence Based on Entrepreneurial Learning Theory
Bibliographic record
Abstract
For promoting innovation-driven strategies and accelerating economic development and reform, initiative of mass entrepreneurship and innovation in china is started in September 2014. College students are an important force in promoting the initiative. However, according to the 2019 Employment Report on Chinese College Students, only 1.8 percent of college graduates in 2018 started their businesses and the success rate was even lower because of lacking entrepreneurial competence. This paper aims to arouse the attention of government institutions, universities, teachers, and college students to entrepreneurship curriculum by exploring the relationship of entrepreneurship knowledge literacy curriculum on college graduates' entrepreneurial competence. This study collected 400 data by surveying senior students in typical experience universities and used AMOS23.0 and SPSS26.0 as data analysis tools. Results demonstrated that entrepreneurship knowledge literacy curriculums directly and significantly affect college students’ entrepreneurial competence and entrepreneurial self-efficacy has the mediating effect. However, entrepreneurship knowledge literacy curriculums do not affect entrepreneurial skills competence. This study suggests that typical experience universities of innovation and entrepreneurship should appropriately increase entrepreneurship practice curriculums to cultivate college students’ entrepreneurial skill competence.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".