Big Data and Learning Analytics Model for Promoting Physical Literacy in College Students in China

Abstract

Physical literacy has gained much popularity in educational circles who are working on the improvement of curriculum and overall standard of education. It involves a holistic lifelong comprehensive learning approach that includes movements and physical activities. Overall, it has positive effects on physical, psychological, social, and cognitive health of individuals, so physical literacy exemplifies the dedication to raise a healthier, more active generation. Numerous factors interacting between humanities and social sciences affect the promotion of physical literacy, so such a study will be interdisciplinary which will consider across all social and individual factors. The current research proposes a system dynamic “SD” model to promote students’ physical literacy by building a complete causal loop diagram of the model to illustrate the general system. Based on the casual loop diagram, the system is then presented as four subsystems. The model is simulated by allocating 14 different changes of indexes in the physical literacy promotion system to find better allocations for optimal effectiveness in promoting physical literacy. Simulations are carried out by using the Apache Spark architecture utilizing “Big Data” tools for effective, speedy, and reliable analysis and results. The study proposes that different physical literacy indexes in different grades require attention; the optimal promotion of physical literacy can be achieved by increasing the physical knowledge of lower-grade students and increasing the physical attitude of higher-grade students. The model can be used to make decisions about efficient physical literacy management and physical literacy promotion planning.