Integrating constructivist principles in an adaptive hybrid learning system for developing social entrepreneurship education among college students

Abstract

Education in social entrepreneurship for college students is a gradual and evolving process that shapes their career outlook and skill development. This journey involves employing various strategies to engage with and analyze innovative concepts. Adaptive hybrid learning systems adjust course materials dynamically based on individual students' skills and learning progress, drawing from diverse learning theories and instructional interventions. Empowering students to drive positive social change is a central focus, with social entrepreneurs tackling global challenges such as poverty, unemployment, gender inequality, inadequate education, healthcare, and governance. While some students may find traditional classroom settings inconvenient and prefer the flexibility of hybrid learning, others weigh the risks and benefits of classroom instruction differently. Data Envelopment Analysis (DEA) is utilized for empirical analysis, utilizing early-stage social entrepreneurship education as a performance measure and cultural probabilities as variables. The significance of college students' contributions through social enterprises is increasingly recognized, prompting the development of Convolutional Neural Network (CNN) models to forecast and assess career growth trends. The DEA-CNN framework aims to enhance adaptive hybrid learning systems by evaluating social entrepreneurs' knowledge, skills, and competencies. Social entrepreneurship not only addresses environmental concerns but also enriches cultural diversity. Many college students prioritize practical action over mere ideation, particularly in entrepreneurship.