Reconceptualizing Localized Internationalization: A Pedagogical Framework for Integrating Big Data Research in STEM Education

Abstract

This study advances a theoretical framework for localized internationalization in Big Data Education by repositioning region-specific data ecosystems as innovation catalysts rather than cultural constraints. Grounded in the OECD Global Skills Framework and empirical analysis of 182 Science, Technology, Engineering, and Mathematics (STEM) students across 5 applied universities (3 in Asia, 2 in Africa), we replaced Western-centric case studies with locally generated data examples (e.g., Jakarta's AI traffic optimization, Lagos' cross-border e-commerce analytics). Through a mixed-methods design (quantitative surveys, project analysis, focus groups), we demonstrate three critical outcomes: (1) 22.3% higher research participation (p<0.001); (2) 84.7% of students reported content as "culturally grounded yet globally transferable"; (3) 72.8% of projects achieved cross-border applicability. Crucially, our framework resolves the epistemic injustice inherent in Western-centric curricula by embedding local data as methodological assets. We identify two pedagogical mechanisms driving this: (a) contextual scaffolding (leveraging students' lived experiences to build analytical frameworks), and (b) research co-creation (connecting students to national data initiatives). This work challenges the hegemony of Western knowledge production in STEM education, offering a replicable model for emerging economies to lead in Big Data innovation.