Compiling Integrated Entrepreneurship Module by Using Design-Based Research Approach to Improve Students’ Entrepreneurial Skill
Bibliographic record
Abstract
This study aimed to develop a complete practical comprehensive module to improve students’ entrepreneurial skills, particularly to their managerial aspects. Research and development approach with design based research (Elly and Levy, 2010) was employed as the technique of this present study. For data collection and analysis throughdesign-based approach, several procedures were conducted, which involved problem identification, objectives framing, product design and development, product examination and test, result evaluation, and result communicating. The result of this research showed that the integrated entrepreneurship module was ultimately able to improve the students’ capability (80%) in both explaining and applying the concept of managerial aspects and entrepreneurial skills. As the result, this research finally produced an integrated module containing 3 chapters and 12 topics. Chapter 1 described on how to initiate entrepreneurial passion and consisted of 5 complete topics. Chapter 2 described on how to manage and run a business and it contained 6 topics. Chapter 3 described on how to implement a business plan and it consisted of 1 topic only. With this module, 80 % students were found successful understanding the given materials and implementing the management aspects in their entrepreneurial practice. Thus, the null hypothesis proposing that students’ entrepreneurial skills would be significantly improved was considered supported.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".