Going Beyond Intentions: A Methodology For Assessing Entrepreneurial Activity Among Engineering Education Alumni

This research paper proposes a novel methodology for evaluating entrepreneurial activity among engineering education alumni using their public CVs as our main source of information. The objective is to go beyond measuring entrepreneurship intentions or mindset through surveys, and instead analyse actual career data to assess the impact of entrepreneurship education. The study utilises shared user data and employs GPT (Generative Pretrained Transformer) models to infer entrepreneurial activity that extends beyond job titles, delving into the specific responsibilities and achievements associated with each position. The analysis shows that the proposed methodology, which uses context enriching to enhance model accuracy, effectively identifies instances of entrepreneurial activity among CVs profiles data. This approach provides a way to evaluate the effectiveness of entrepreneurship and innovation courses. Combining the insights gained through the proposed method with internal data sources would enable institutions to conduct a comprehensive evaluation of program impact on alumni career paths. The study underscores the potential of AI models to facilitate the collection and analysis of data that has traditionally been challenging to access. Moreover, the research highlights the importance of evaluating the long-term impact of entrepreneurship education on alumni career trajectories, a key factor in addressing the growing field of engineering education. Ultimately, this study contributes to the academic discourse on entrepreneurship education by offering a novel approach for assessing the impact of such programs on alumni outcomes, thus enabling institutions to make data-driven decisions to improve program offerings.