Abstract:
The study of entrepreneurship education (EE) is a thriving subject of study. Due to the limited insights offered by a portion of EE rather than the entire corpus, existing research is constrained. This study aims to comprehensively review entrepreneurship education's effectiveness and theoretical underpinnings to meet this requirement. This study examines academic research on entrepreneurial education using big data analytics and machine learning. The "most significant articles and top contributing journals, authors, institutions, and nations" for entrepreneurship education are dissected in this study. Additionally, this study clarifies seven important EE research themes: Gender and pedagogy, innovation and technology transfer, entrepreneurial intention, entrepreneurial mindset, entrepreneurial learning, entrepreneurial ecosystem and self-efficacy, and theory of planned behavior of entrepreneurship education. This paper makes numerous recommendations for future EE research to help advance the field, including the necessity of a sizable sample set to assess entrepreneurial exposure and entrepreneurial action; evaluate the ecosystem's components and how various stakeholders collaborate; need to look into the relationship between "extracurricular activities, entrepreneurship selfefficacy, entrepreneurial intention, and entrepreneurial behaviors;" and to establish the relationship between "entrepreneurship self-efficacy, entrepreneurial intention, and entrepreneurial behaviors."
