Crafting Effective Design Principles for Science and Technology-based Entrepreneurship Education
Bibliographic record
Abstract
In the realms of science and technology, higher education institutions rely on educators to drive innovative changes and foster entrepreneurial skills for the commercialization of scientific breakthroughs (Forliano et al., 2021; Klofsten & Jones-Evans, 2000; Siegel & Wright, 2015). However, a notable gap exists in scholarly literature concerning this domain (Blankesteijn et al., 2021; Harms, 2015; Tiberius & Weyland, 2023). Traditional science and technology programs often fail to adequately prepare students for entrepreneurship compared to scientific pursuits alone (Duval-Couetil et al., 2020). The formulation of design principles is crucial, serving as the foundation for developing entrepreneurship educational interventions (Naia, 2014; Baggen et al., 2022; Goksen-Olgun, 2022). These principles facilitate reflection on existing programs, aid in developing new ones, and enable comparison and research. Despite various proposed approaches to entrepreneurship education (EE), there is a lack of focus on the "why" and "whom" dimensions in current literature (Naia, 2014; Baggen et al., 2022; Goksen-Olgun, 2022). Entrepreneurship education is key to the commercialization process, supported by evidence-informed discussions among educators. Thus, identifying and applying EE design principles specific to science and technology domains is crucial for enhancing pedagogy and addressing scholarly literature gaps (Duval-Couetil et al., 2020).<br/><br/>In this interactive workshop, practitioners exchange knowledge on entrepreneurship education didactics in a science and technology-based domain. Through sharing of real-world practices, tools and experiences we co- create insights that lead to a set of domain specific design principles. Design principles serve as a foundation for systematically developing entrepreneurship educational interventions for educators (Baggen et al., 2022). The mini world cafe format is a scientific enquiry which facilitates structured discussion in small groups and provides empirical data to researchers whilst encouraging collaboration (Schiele et al., 2022). The objective of this session is to consider the current design principles in entrepreneurship education practices and define design principles tailored to education in the science and technology domain.<br/><br/>The workshop is aimed at (entrepreneurship) educators, practitioners, and professionals such as venture creation managers who seek to enhance entrepreneurship education within the science and technology domain. During the workshop practitioners share their current practices and insights for themes to emerge providing preliminary findings for further study as the Researcher aims to formulate domain-specific design principles. The session concludes with a process of voting for the most key entrepreneurship education principles which would align with the demands of a science and technology entrepreneurship program.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".