Motivation In Physical Education (PE) Learning Through Online System

Da'i, Mohamad; Cahyani, Olivia Dwi; S, Adi

doi:10.33369/jk.v5i1.14436

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…The results show that through the online learning system physical education subjects of SMA + AL Fatimah Bojonegoro in class X 2020/2021, the students' learning motivation belongs to the "moderate" group, which is 73.21%. The learning motivation of PE students belongs to the middle category [5]. Perumal I proposed an adaptive learning system framework.…”

Section: Related Workmentioning

confidence: 99%

Design of the Online Learning System for College Physical Education Teaching Based on the BP Neural Network

Bin

Wang

2022

Wireless Communications and Mobile Computing

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The popularization of the campus network provides the possibility for the realization of online sports teaching. Based on the current situation of college physical education, the existing electronic textbooks, individual technical education software, educational videos, and other resources are neatly integrated. It builds a sports network education platform that is helpful for students to learn and realizes open interactive education. Nowadays, with the development of the Internet, artificial intelligence technology has solved many problems for people. The learning model combining artificial intelligence and online education has also become a new trend of educational informatization. This paper is based on the BP neural network in artificial intelligence to explore the design of the college sports teaching system in school. The purpose of this paper is at developing an online learning system suitable for college sports, so as at improving the learning efficiency and learning experience of college sports students. This paper firstly introduces the background and research significance of online learning, the BP neural network. Then, it introduces the design of the BP neural network and online system; in this paper, a series of introductions are given to its structure, function, and recommendation algorithm. Afterwards, this paper tests the performance of the designed system and finds that the average response time of the system is 0.978 s. Finally, this paper does a questionnaire survey to the users of the learning system. It is found that both in the recognition and in the liking sections, the satisfaction of students and teachers is relatively high.

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Section: Related Workmentioning

confidence: 99%

Design of the Online Learning System for College Physical Education Teaching Based on the BP Neural Network

Bin

Wang

2022

Wireless Communications and Mobile Computing

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“…As an alternate, models are defined as a setting created to improve a person's involvement with real situations, which can help a person build skills and learning goals. Institutions, secondary and elementary campuses, and other training centers were forced to solely offer educational software when the new monarch pandemic broke out in 2020 and in response to the cry for "rescinding courses and not learning" [6]. Online learning has so far progressed and evolved into a brand-new educational paradigm for the Age of the internet.…”

Section: Introductionmentioning

confidence: 99%

Design of intelligent assistive system for physical education: based on personalized training plan

Liu

2024

jes

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Instead of viewing different components in solitude, teachers' opinion with respect and pedagogical knowledge demands a comprehension of the relationships connecting them. The instructor must be knowledgeable about the subject matter, comprehend the most effective delivery methods for the information, be acquainted with the traits of the children, and be aware of the academic setting to educate in that field effectively. Sport is still undergoing economic, cultural, and ethical change. On the other side, throughout the past century, science has been the sport's most pervasive transformation. Players can now jump higher, run faster, and, importantly—retain their health due to scientific understanding. Even though scholars, organizations, and governments have indeed encouraged physical education instructors to incorporate software in their classes, technology is often employed for routine duties, such as tracking enrollment and assessing, recording, and reporting children’s' progress. In order to estimate posture in physical training, this research proposes a continuous filter convolution neural network. The approach also assesses the learners' understanding, memory, and accomplishments and offers suggestions for enhancements and remedial actions. The framework and conventional teaching-learning methodologies are then compared characteristics per aspect for output criteria. Finally, the classification algorithm is contrasted with other deep learning algorithms, and it is found that the proposed ConFil_CNN achieves 98.5% accuracy, 96.7% of precision, 93.5% of recall, 95.2% of F1-score and 12.4ms of response time.

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“…Comparison experiments with traditional algorithms show that the algorithm has good performance classification effect. Taking the development of online sports as the theme and the establishment of a sports network auxiliary system as the content, the research on the auxiliary role of network technology in physical education is studied [8]. In the designed college sports system, physical education teachers can learn the online environment through the Internet, and they can prepare courses and PPT and use online technology to upload videos, post homework, and post test standards and results [9,10].…”

Section: Introductionmentioning

confidence: 99%

Smart System Design for College Physical Education Class Based on Abnormal Audio Detection Algorithm

Bai

2022

Mobile Information Systems

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Based on dual-core technology and the theory of nontransmission and noninterference of information flow, this paper conducts a detailed system trust environment investigation on key issues such as trust routing design and reliability analysis in the embedded structure. At the same time, we will study the method of extracting features from speech data. When dealing with abnormal audio detection, firstly, the preprocessed valid audio clips are framed and windowed and have stable short-term characteristics; after the feature is stable, the frequency feature and time domain feature of audio data are analyzed and compared. And then combined with specific applications, a detailed demand analysis is carried out, and the development plan and implementation method of the college physical education network auxiliary system are proposed. The teaching system follows a three-tier system structure. In order to expand the existing functions, this paper uses the principle of modularity, starting from the following four aspects: educational resources, online question and answer, coursework, and test-related modules. We choose object-oriented and easy-to-expandable modules, such as the development and implementation of programming language environments and database systems. By applying the abnormal audio detection technology to the embedded system of college P.E. classroom, it can effectively optimize the traditional P.E. teaching mode and promote the teaching efficiency and the new development of P.E. teaching in the information age.

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Motivation In Physical Education (PE) Learning Through Online System

Cited by 5 publications

References 3 publications

Design of the Online Learning System for College Physical Education Teaching Based on the BP Neural Network

Design of the Online Learning System for College Physical Education Teaching Based on the BP Neural Network

Design of intelligent assistive system for physical education: based on personalized training plan

Smart System Design for College Physical Education Class Based on Abnormal Audio Detection Algorithm

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