Preparation of a Learning Module for Entrepreneurship Course at Economic Education Study Program of Faculty of Teacher Training and Education Sriwijaya University
Bibliographic record
Abstract
The objective of this study was to prepare teaching materials in the form of entrepreneurship learning module to be used as a handbook in the classroom learning process. Entrepreneurship lecture of study program at FKIP UNSRI has various material differences delivered in lecturing activity. One of the objectives to be achieved in this study was to obtain a general description of the entrepreneurship learning materials that should be the subject. 14 materials obtained from the results of data processing from questionnaires given to lecturers of entrepreneurship courses were as follows: the scope of entrepreneurship, determination of ideas and entrepreneurial opportunities, business plan, innovation and creativity in entrepreneurship, the concept of management in entrepreneurship, marketing strategy and the concept of Break Event Point (BEP), subject of entrepreneurial ethics, entrepreneurial mindset, competitive strategy, motivation theory in entrepreneurship, risk management of customer behavior and the path to successful entrepreneurship. The materials obtained from this data processing were then used as a guide for the preparation of entrepreneurship learning modules starting from the preliminary study, in the form of needs analysis of the learning module and found out that entrepreneurship learning module needed to be prepared to support the achievement of learning objectives. The next step was to map the module based on the syllabus to obtain the title of the module developed, followed by the preparation of the opaque module and the module writing stages containing the introductory section (introduction, table of contents, list of figures, list of tables, description, module usage guide, glossary), section of learning materials (14 Materials) and references. Finally the entrepreneurship learning module for the students was prepared.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.029 |
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".