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A teaching management system for physical education in colleges and universities using the theory of multiple intelligences and SVM

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Abstract

The level and style of teaching management have been continuously updated and enhanced as part of China's educational system reform in recent years. A new era of individualized learning and teaching administration has begun as a result of the fusion of cutting-edge educational technologies and pedagogical theories. In light of this, we introduced a teaching management system created for the ever-changing needs of physical education in colleges and universities. We use support vector machines (SVM) to personalize and improve students' educational experiences by using the Theory of multiple intelligences (MI) as our theoretical framework. In the very first step, we started with the collection of diverse student information, such as academic records, athletic prowess, and many intelligence profiles. To obtain this information, multiple skills of all the students were thoroughly analyzed using MI’s theory. By conducting thorough feature engineering and data preprocessing, we established the foundation for our analysis using SVM. In this phase, we score each student's performance in sports-related skills. From there, we created a profile for each student and preprocessed the data to build a useful dataset. This dataset trains an SVM model to forecast student engagement and performance using specific kernels and hyperparameters. Our proposed system places a big emphasis on personalized learning. We enable teachers to design a learning environment that fosters student potential by adapting teaching tactics to individual student's strengths and limitations in both sports skills and multiple intelligences. The effectiveness of the system is assessed by separating the students into two groups: one group received training using our suggested system, known as the experimental group, and the other group, known as the control group, received training using the conventional approach. The result shows that the score of the experimental group is 88.9, which is higher than the control group's 83.5. The result shows that after applying MI’s theory and SVM in physical education teaching in colleges and universities, the scores have been significantly improved, which can maximize the students' multiple intelligences.

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Authors and Affiliations

  1. College of General Education, Liaoning University of International Business and Economics, Dalian, 116052, Liaoning, China

    Jia Ding

  2. College of Sports Science, Shenyang Normal University, Shenyang, 110034, Liaoning, China

    Ying Su

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  1. Jia Ding
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  2. Ying Su
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Correspondence to Ying Su.

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Ding, J., Su, Y. A teaching management system for physical education in colleges and universities using the theory of multiple intelligences and SVM. Soft Comput 28, 685–701 (2024). https://doi.org/10.1007/s00500-023-09419-8

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