Research on Effect Evaluation of Physical Education Teaching Based on Artificial Intelligence Expert Decision Making System

Ji, Wen

doi:10.1007/978-3-319-60744-3_31

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…However, existing classroom teaching decision-making methods often fail to meet students' personalized learning needs, resulting in some students being unable to reach their full potential in the classroom [14][15][16][17]. It is difficult for those traditional methods to fully consider the personalized needs of students, because their teaching design mainly relies on the teaching syllabus and the personal experience of teachers [18][19][20][21]. In this situation, the teaching contents and methods are often not diversified, which is not conducive to stimulating students' interests and initiative in learning.…”

Section: Introductionmentioning

confidence: 99%

Personalizing Students' Learning Needs by a Teaching Decision Optimization Method

Zhang,

Shi,

2023

Int. J. Emerg. Technol. Learn.

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With the rapid development of educational technology and the deepening of educational system reform, personalized education has gradually become an important topic in education. However, existing classroom teaching decision-making methods often fail to meet students’ personalized learning needs, resulting in some students being unable to reach their full potential in the classroom. To solve this problem, this study proposed a multi-conditional factor classroom teaching decision optimization method based on the improved particle swarm optimization (IPSO) algorithm, and predicted students’ personalized learning needs by combining with the improved ant colony optimization-support vector regression (IACO-SVR) model. First, the IACO-SVR model was used to collect students’ learning data, such as grades, interests, hobbies and learning progress, to accurately predict their needs in different teaching contexts. Second, the IPSO algorithm was used to optimize the multi-conditional factor classroom teaching decisions, thus meeting the personalized needs of students. The IPSO algorithm had strong global search ability, which effectively found the optimal solution to achieve personalized teaching strategies. It is expected that the teaching quality can be improved by predicting the personalized learning needs of students and optimizing classroom teaching decisions in this study, thus providing better support for their comprehensive development. In addition, the results of this study can provide theoretical basis and reference for administrative departments of education and schools to formulate personalized education policies.

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Section: Introductionmentioning

confidence: 99%

Personalizing Students' Learning Needs by a Teaching Decision Optimization Method

Zhang,

Shi,

2023

Int. J. Emerg. Technol. Learn.

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“…STEM education originates in the United States. It integrates science, technology, engineering and mathematics [4,5]. The original purpose of the proposal is to promote the integration of multiple disciplines, strengthen the close relationship between disciplines, break the relatively independent state of each discipline, and encourage students to use the knowledge of multiple disciplines to explore and solve problems, cultivate their thinking ability and pursue the spirit of science [6,7].…”

Section: Introductionmentioning

confidence: 99%

Design and Evaluation of Science Teaching Using STEM Literacy

Chen¹,

Li²,

Liu³

2019

Proceedings of the 2nd International Workshop on Education Reform and Social Sciences (ERSS 2019)

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Research on college physical education teaching effect evaluation based on probabilistic hesitant fuzzy QUALIFLEX method

Li-juan

Hou

2023

IFS

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The evaluation of physical education teaching effectiveness is an important component of physical education teaching, and plays a multifaceted role in the process of physical education teaching. The information provided by it can control and regulate the progress of physical education teaching activities as a whole, ensuring that physical education teaching activities develop towards predetermined goals. With the development of the popularization of physical education, people’s requirements for the quality of physical education continue to improve, and the role and position of evaluation in teaching has become increasingly evident. Evaluation of physical education teaching effectiveness has become an indispensable process in teaching activities. The college physical education teaching effect evaluation can be regarded as a multiple attribute decision making (MADM). Thus, this paper collected information in probabilistic hesitant fuzzy sets (PHFSs) and using CRITIC method to obtain the unknown weight among attributes. Further, a novel probabilistic hesitant fuzzy QUALIFLEX (PHF-QUALIFLEX) method was constructed for MADM. Finally, a numerical case for college physical education teaching effect evaluation was illustrated with this proposed model and other methods were utilized to compare with PHF-QUALIFLEX method to verify the feasibility and applicability.

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Research on Effect Evaluation of Physical Education Teaching Based on Artificial Intelligence Expert Decision Making System

Cited by 3 publications

References 8 publications

Personalizing Students' Learning Needs by a Teaching Decision Optimization Method

Personalizing Students' Learning Needs by a Teaching Decision Optimization Method

Design and Evaluation of Science Teaching Using STEM Literacy

Research on college physical education teaching effect evaluation based on probabilistic hesitant fuzzy QUALIFLEX method

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