Design of an Instant Data Analysis System for Sports Training Based on Data Mining Technology

Introduction

The quality of education offered by a school directly affects the vitality of that school, whose core mission is to educate its students (Hui & Jin, 2021). Sport is a highly integrated discipline, encompassing athletics, humanities, and social sciences (Yin & Cui, 2021). In the past, the research field of real-time data analysis systems was limited to exercise training, exercise evaluation, and exercise management. (Rajšp & Fister, 2020). In recent years, guided by the principle that science and technology are primary drivers of productivity, China’s sports departments have increasingly recognized the importance of technological advancement, leading to enhanced scientific decision making and management in sports (Bonidia et al., 2018). Moreover, as the scale of education expands, the training process generates vast quantities of data, making this data increasingly difficult for educational decision makers to understand. Traditional data processing methods are ill-equipped to handle this surge in data accumulation (Karachi et al., 2017). Given this state of affairs, teachers must convert scores recorded in minutes, seconds, and meters to percentages based on national physical fitness standards (Afzali & Mohammadi, 2018). At the same time, each teacher must record the converted scores using the classroom’s educational management software (Zhang & Mao, 2021). With these vast amounts of data, the existing database management methods and data statistics methods are increasingly unable to adapt to the national proposal of “healthy exercise” and sports talent stratification (Hu, 2018). Data based on various sports indicators lacks interconnectedness (Wang & Chen, 2017). Therefore, if we do not adopt advanced management concepts, change the school philosophy, deeply understand the diverse needs of society, understand the characteristics of each student, and incorporate specific specialties, it will be difficult to apply the original management methods and teaching methods, and sustainable development will become increasingly challenging (Gao et al., 2018). Current methods offer only basic queries and statistics, without in-depth analysis to identify factors impacting student performance (Wang & Liang, 2021). Data mining (DM) and data warehousing techniques can better achieve this latter objective (Gamonales et al., 2021). DM excels in both identifying and solving problems. It is the process of extracting hidden but potentially useful information and knowledge from large amounts of noisy real-world application data (Choi & Yoon, 2017). DM and knowledge discovery have advanced data processing techniques (Kantilal & Sharma, 2020). It not only allows in-depth analysis of data, but also provides the necessary information in a timely and accurate manner, which enables the deep search for interrelationships between various elements within an immense amount of data (Park et al., 2020), as well as guiding the creation of new rules for learning and classroom training in school sports. A large amount of sports data is being accumulated in the fields of sports competition and sports industry. Sports researchers now face the vital task of using these data to discover information that is useful but easily overlooked.

The innovations of this paper are:

• (1)

  The aim of researching a real-time data analysis system for PE based on DM technology is to boost teachers’ efficiency and accuracy, thus freeing them from monotonous tasks.

• (2)

  Using DM helps identify underlying factors that impact educators’ teaching, thus providing suggestions for improving the quality of teaching.

• (3)

  DM technology is applied to a real-time data analysis system in the field of physical training, using a large amount of experimental data to establish a data warehouse that matches the physical capabilities of college students.

The paper is structured as follows: