Needs Assessment for the Development of Entrepreneurship Curriculum for a Master’s Degree Program
Bibliographic record
Abstract
The objective of this study is to study the opinion of entrepreneurs toward an entrepreneurship degree, to study the opinion of bachelor’s degree students toward a master’s degree in entrepreneurship, and to study the guideline for a master’s degree in an entrepreneurship program. In this study, we used the quantitative method within the questionnaire provided to entrepreneurs and bachelor’s degree students, and we analyzed the results using mean and standard deviation. We also utilized a qualitative method using small group discussion by inviting five academics to discuss the guidelines for a master’s degree in an entrepreneurship program. The results of this study show that the entrepreneurial skills most required are communication and collaboration, the skill of teamwork is higher amongst graduates from master degree programs, and that most bachelor degree students who wish to study in the graduate program think about job opportunities first (in both the public and private sector) and hope that graduate study will increase their knowledge, skills, experience from knowledge and knowledge-sharing in class, and will result in a new way of thinking. In addition, problem-based learning and active learning are very important for a master’s degree program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".