Impact of Chatbots on Student Learning and Satisfaction in the Entrepreneurship Education Programme in Higher Education Context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are many ways to learn how to be entrepreneurs and one of the powerful ways is to learn from successful entrepreneurs. However, it is difficult to reach and interview those entrepreneurs about their best practices in doing business in real lives. Chatbot technology can come into play in mimicking conversation of successful entrepreneurs and providing pre-programmed responses of their best practices drawn from interviews published in newspapers, books and articles. Therefore, this research aimed to examine the impact of chatbots in the form of successful entrepreneurs with 24 first-year graduate students, who enrolled in a master's degree of entrepreneurship education at Kasetsart university. Data analysis involved mean, standard deviation, frequency, percentage, and content analysis. The research findings showed that the developed chatbots were appropriate at a very high level (Mean= 4.75, S.D. = 0.22). The impact of chatbots was positive. Students perceived that their learning was better and their satisfaction was at a very high level (Mean = 4.65, S.D. = 0.44) with thoughts that chatbots were an interesting, innovative, and fun teaching way. This study indicated that chatbot technology positively impacted student learning and satisfaction. It can be implemented as a powerful tool to teach entrepreneurship in entrepreneurship education programmes in higher education context.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it