Realization of intelligent computer aided system in physical education and training

被引:0
作者
Hu Y. [1 ]
机构
[1] Department of Sports, Henan Institute of Technology, Henan, 453003, Xinxiang
来源
Computer-Aided Design and Applications | 2020年 / 18卷 / S2期
关键词
Auxiliary Teaching; Badminton Teaching; Experimental Research; Intelligent Computer Aided System;
D O I
10.14733/cadaps.2021.S2.80-91
中图分类号
学科分类号
摘要
With the introduction of artificial intelligence technology, computer-aided instruction (CAI) has developed into intelligent computer-aided instruction (ICAI). ICAI not only overcomes many weaknesses of traditional CAI, but also greatly improves and improves the teaching effect and efficiency. This article first analyzes and studies the characteristics and functions of the intelligent computer-aided instruction system, and gives a corresponding implementation framework on this basis. Then, using mathematical statistics, logical analysis and other methods, randomly select two ordinary class undergraduate students as the investigation experiment object, using the content of the current badminton syllabus as the teaching experiment content, try to construct the teaching of "using intelligent computer-assisted badminton teaching" Model flow chart, this experiment draws the use of intelligent computer-assisted teaching methods in badminton teaching. Teachers create effective teaching programs according to the teaching goals, give full play to the student’s dominant position, and carry out targeted teaching, which can effectively improve badminton lessons. The use of intelligent computer-assisted badminton teaching enhances the communication and exchanges among students, and cultivates the spirit of mutual cooperation, mutual help and mutual progress among students. The use of intelligent computer-assisted badminton teaching has fundamentally achieved the principles of student's subjectivity and comprehensive development of body and mind, which has further stimulated students' initiative and enthusiasm in learning badminton and cultivated students' lifelong sports awareness. © 2021 CAD Solutions, LLC,.
引用
收藏
页码:80 / 91
页数:11
相关论文
共 10 条
[1]  
Chen S., Zhang K., Jia X., Qiang M., Chen Y., Evaluation of the computer-assisted virtual surgical technology in preoperative planning for distal femoral fracture, Injury, 51, 2, pp. 443-451, (2019)
[2]  
Andres E., Reichert S., Brandt C., Hill N., Gass R., Development and experimentation of a new digital communicating and intelligent stethoscope, European Research in Telemedicine/La Recherche Européenne en Télémédecine, 5, 4, pp. 145-155, (2016)
[3]  
Abele E., Chryssolouris G., Sihn W., Metternich J., ElMaraghy H., Seliger G., Seifermann S., Learning factories for future oriented research and education in manufacturing, CIRP annals, 66, 2, pp. 803-826, (2017)
[4]  
Brawner K., Sinatra A.-M., Sottilare R., Motivation and Research in Architectural Intelligent Tutoring, International Journal of Simulation and Process Modelling, 12, 3, pp. 300-312, (2017)
[5]  
Burkhardt J.-M., Corneloup V., Garbay C., Bourrier Y., Jambon F., Luengo V., Job A., Cabon P., Benabbou A., Lourdeaux D., Simulation and Virtual Reality-Based Learning of NonTechnical Skills in Driving: Critical Situations, Diagnostic and Adaptation, IFAC-PapersOnLine, 49, 32, pp. 66-71, (2016)
[6]  
Cabestrero R., Quiros P., Santos O.-C., Salmeron-Majadas S., Uria-Rivas R., Boticario J.-G., Arnau D., Arevalillo-Herraez M., Ferri F.-J., Some Insights into the Impact of Affective Information When Delivering Feedback to Students, Behaviour & Information Technology, 37, 12, pp. 1252-1263, (2018)
[7]  
Chih-Yueh C., Lai K.-R., Po-Yao C., Chung H.-L., Tsung-Hsin C., Negotiation Based Adaptive Learning Sequences: Combining Adaptivity and Adaptability, Computers & Education, 8, 8, pp. 215-226, (2015)
[8]  
Gulshan V., Peng L., Coram M., Stumpe M.-C., Wu D., Narayanaswamy A., Venugopalan S., Widner K., Madams T., Cuadros J., Kim R., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, Jama, 316, 22, pp. 2402-2410, (2016)
[9]  
Kermany D.-S., Goldbaum M., Cai W., Valentim C.-C., Liang H., Baxter S.-L., Dong J., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell, 172, 5, pp. 1122-1131, (2018)
[10]  
Preim B., Saalfeld P., A survey of virtual human anatomy education systems, Computers & Graphics, 7, 1, pp. 132-153, (2018)