Design of a Personalized Teaching Path for the Intelligent Sports Engineering Micro-major Based on Big Data Analysis

Abstract

With the accelerated digital transformation of the sports industry and the increasingly prominent demand for personalized higher education, the Intelligent Sports Engineering Micro-major, as an interdisciplinary educational model, urgently requires the establishment of a data-driven teaching paradigm. Supported by big data analysis as the core technology, this study aims to construct a personalized teaching path for the Intelligent Sports Engineering Micro-major. The research first explains the integration mechanism and theoretical foundation combining the Intelligent Sports Engineering Micro-major with big data technology, analyzing its interdisciplinary essence and theoretical bases such as constructivism and connectivism. It then systematically examines the key elements of personalized teaching based on big data analysis, including multi-dimensional learner profile construction, dynamic competency model establishment, and adaptive resource delivery mechanisms. Finally, addressing implementation challenges such as data quality, model accuracy, and systematic support, the study proposes dynamically optimized strategies based on feedback loops to form a sustainable evolutionary teaching path design. This provides theoretical reference and practical paradigms for talent cultivation in the field of intelligent sports engineering.