Abstract
Learning has been a significant emerging field for several decades since it is a great determinant of the world’s civilization and evolution, having a significant impact on both individuals and communities. In general, improving the existing learning activities has a great influence on the global literacy rates. The assessment technique is one of the most important activities in education since it is the major method for evaluating students during their studies. In the new era of higher education, it is clearly stipulated that the administration of higher education should develop an intelligent diversified teaching evaluation model which can assist the performance of students’ physical education activities and grades and pay attention to the development of students’ personalities and potential. Keeping the importance of an intelligent model for physical education, this paper uses factor analysis and an improved random forest algorithm to reduce the dimensions of students’ multidisciplinary achievements in physical education into a few typical factors which help to improve the performance of the students. According to the scores of students at each factor level, the proposed system can more comprehensively evaluate the students’ achievements. In the empirical teaching research of students’ grade evaluation, the improved iterative random forest algorithm is used for the first time. The automatic evaluation of students’ grades is achieved based on the students’ grades in various disciplines and the number of factors indicating the students’ performance. In a series of experiments the performance of the proposed improved random forest algorithm was compared with the other machine learning models. The experimental results show that the performance of the proposed model was better than the other machine learning models by attaining the accuracy of 88.55%, precision of 88.21%, recall of 95.86%, and f1-score of 0.9187. The implementation of the proposed system is anticipated to be very helpful for the physical education system.
Highlights
Education is an important and a fascinating field which grows over the years and has a significant impact on everyone’s life
The dataset selected for the experimental work is the student achievement dataset. e dataset of students’ achievement and characteristics was collected from the course of college students’ public physical education. e dataset includes 9 characteristics/attributes of students. e nine characteristics are divided into three categories, i.e., students’ statistical characteristics, educational background characteristics, and students’ behavior characteristics. e classification of students’ behavior characteristics includes students’ activity in the class, i.e., students’ absence, the number of times students visit teaching resources after the
Normalization and Characterization of Dataset Features. e dataset is collected by the author from daily physical education work, which involves multidimensional original data collection. e work involves a long time range, and the workload is relatively large. en, the data preprocessing for the original data is carried out, including data cleaning, data discretization, removal of missing values, data filtering, and so on
Summary
Received 19 July 2021; Revised 21 August 2021; Accepted 29 August 2021; Published 10 September 2021. In the new era of higher education, it is clearly stipulated that the administration of higher education should develop an intelligent diversified teaching evaluation model which can assist the performance of students’ physical education activities and grades and pay attention to the development of students’ personalities and potential. Keeping the importance of an intelligent model for physical education, this paper uses factor analysis and an improved random forest algorithm to reduce the dimensions of students’ multidisciplinary achievements in physical education into a few typical factors which help to improve the performance of the students. According to the scores of students at each factor level, the proposed system can more comprehensively evaluate the students’ achievements. In the empirical teaching research of students’ grade evaluation, the improved iterative random forest algorithm is used for the first time. In a series of experiments the performance of the proposed improved random forest algorithm was compared with the other machine learning models. In a series of experiments the performance of the proposed improved random forest algorithm was compared with the other machine learning models. e experimental results show that the performance of the proposed model was better than the other machine learning models by attaining the accuracy of 88.55%, precision of 88.21%, recall of 95.86%, and f1-score of 0.9187. e implementation of the proposed system is anticipated to be very helpful for the physical education system
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