Enhancing Entrepreneurship Education with Innovatively Designed YouTube Videos: Evaluating Student Learning and Effectiveness of Youtube Videos as Educational Tools
Bibliographic record
Abstract
The use of video media, particularly YouTube videos, has emerged as highly popular and powerful educational tools in higher education due to its ability to provide rich and engaging content that enhances learners' understanding and retention of information throughout the learning process. Much of the current research highlights a need for more active learning strategies to maximize the educational potential of these videos because traditional Youtube videos tend to create passive learning experiences. However, when properly integrated with active learning strategies through innovative video design and development, Youtube videos can enhance student engagement, learning outcomes, and satisfaction. Therefore, this research aims to innovatively design and develop YouTube videos as educational tools and evaluate students' perceptions of their learning and the effectiveness of the video design and development in higher education involving 20 graduate students in the entrepreneurship education program. The data were analyzed using mean, standard deviation, and content analysis techniques. The findings showed that the innovatively developed Youtube videos were perceived to be highly appropriate (Mean= 4.52, S.D. = 0.13). Students reported that they learned entrepreneurship more effectively from various scenarios filled with choices and challenges in entrepreneurial paths. They also perceived the effectiveness of Youtube videos as educational tools to be very high (Mean= 4.58, S.D. = 0.72), noting that these videos were effective tools that promotes active and experiential learning experiences. In conclusion, this study indicates that Youtube videos, when innovatively designed and developed as educational tools with active learning strategies, can be implemented as a powerful tool in teaching entrepreneurship.
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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 | 0.006 |
| 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) | 0.002 | 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".