Construction of College Physical Education MOOCS Teaching Model Based on Fuzzy Decision Tree Algorithm

Abstract

With the continuous development of the MOOCS model in college physical education, the corresponding teaching evaluation has also been widely concerned by the community. The development of a traditional education mode in college physical education cannot meet the current teaching requirements. In order to solve the problem of narrow application and insufficient accuracy in traditional education, on the basis of the Kohonen fuzzy decision tree algorithm and the MOOCS fuzzy decision tree algorithm, a fuzzy evaluation model of P.E. teaching is proposed. The results show that the fuzzy ID3 algorithm can achieve high accuracy in the four databases, and the classification accuracy of the A-D database is 75.9%, 62.9%, 76.6%, and 95.1%, respectively. Except for database C, the classification accuracy of the minimum classification uncertainty algorithm is lower than the other two clear decision tree algorithms. Compared with the minimum classification uncertainty algorithm, the fuzzy ID3 algorithm has obvious advantages in classification rules. The classification rules of A-D database are 18, 12, 16, and 10, respectively. When the authenticity threshold is about 0.8, the fuzzy ID3 algorithm has the highest classification accuracy. The proposed MOOCS model for college physical education based on the fuzzy decision tree algorithm has strong practicability and high accuracy. This paper studies the MOOCS model of college physical education and introduces the fuzzy decision tree algorithm to evaluate the MOOCS model of college physical education. It solves the problem that traditional sports cannot satisfy the needs of “Internet plus education” at all. Compared with the traditional sports model, it has better applicability and higher accuracy.

Improved Decision Support System for College Sports Training Based on Id3 Algorithm

“…In the context of sports training, the DSS utilizing the ID3 algorithm begins by gathering data relevant to athlete performance and training. This includes variables such as player biometrics, physiological metrics (e.g., heart rate variability, oxygen uptake), performance metrics (e.g., speed, agility), injury history, training methodologies, environmental factors, and more [9]. Once the data is collected, the ID3 algorithm is applied to derive decision rules that correlate these input variables with desired outcomes, such as improved performance or reduced injury risk.…”