Evolving students' conceptions about responsible entrepreneurship: a classroom experiment
Bibliographic record
Abstract
Purpose Entrepreneurship education scholarship has been recently challenged to look at what goes on inside the entrepreneurship classroom to assess what students are really learning. Relying on the construction and analysis of a 3-h long set of learning activities on responsible entrepreneurship, this paper focuses on the activities conducted and what students have learned, based on Bloom's revised taxonomy of educational objectives. Design/methodology/approach This paper builds on a pre-/post-intervention assessment around a set of learning activities with 151 undergraduate students. Before and after the class, students were asked to produce a definition of responsible entrepreneurship. They were also asked to reflect on what had changed from the beginning. Findings Analysis of students' pre/post definitions shows a standardization of their conceptions of responsible entrepreneurship. This result confirms that the learning objective of this class was met. Nevertheless, applying Bloom's revised taxonomy to students' reflections allows for more nuanced interpretation. The analysis indeed revealed that some students manifest relatively superficial learning while other shows a deeper ability to reflect on the concept. Originality/value First, this paper contributes to the entrepreneurship education literature by showing the relevance of using Bloom's revised taxonomy for both teaching and research purposes. Second, it presents a set of innovative learning activities on responsible entrepreneurship that could be easily reproduced in other educational contexts. Third, it shows the importance of asking students what they learned and what has changed for them through class activities.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".