Individualized New Teaching Mode for Sports Biomechanics based on Big Data

Ying Liu, Tengqi Zhu


Sports biomechanics is an applied discipline with relatively strong theoretical knowledge. At present, it is used as an analysis means in exercise training in various countries and plays a huge promotion role for the development of competitive sports and sports science. However, phenomena, such as single-teaching methods, fixed-teaching thinking, backward-teaching environment, and hardware, still exist in the current education for sports biomechanics. For such phenomena, we perform individualized IRDC (Internet + retrieval literature + big data + cloud) teaching by using the Internet big data analysis and considering different characteristics of every student and apply it in online teaching of sports biomechanics course. We base our proposed individualized IRDC teaching mode on postmodern curriculum theory. According to four features, namely, rich, recursive, relational, and rigorous, we propose an individualized IRDC online teaching mode in this study. Moreover, we apply a new wireless telemetry surface EMG tester as a learning tool of practical teaching in the teaching mode, which we use to acquire Internet information data of online course and induce a real time communication between teachers and students. Finally, we adopt the principal component analysis to develop evaluation indices, including expert evaluation in and out of school and peer teacher evaluation, for the teaching mode. We find through teaching practice that the proposed individualized IRDC teaching mode can make the best of advantages of big data teaching, help teachers implement targeted individualized teaching, and contribute to the improvement of students' academic performance and comprehensive qualities.


Smart classroom; interaction; visualization; interaction analysis system

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Copyright (c) 2020 Ying Liu, Tengqi Zhu

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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