The Role of Gross Motor Performance Towards Hand-Eye Coordination Among Girls Aged 7 Years Old in Malaysia Using Machine Learning

Abstract

Range of motor performance is a prerequisite for children to participate in daily activities and has significant consequences for different aspects of children and adolescent's growth. The objective of the study is to clusterize the coordination performance, to validate the role of gross motor performance towards classification of hand-eyes coordination and to develop best fit model among girls aged 7 years old in Malaysia. The sample size of this study was calculated using G power software. The statistical power was set at 95% with an effect size of 0.50%. According to [1], an effect size of +0.20 was considered as small, +0.50 as medium and +0.80 as strong. Population for this analysis were student of 7-year-old girls in primary schools in peninsular Malaysia and Borneo. In this study anthropometric measurement and motor performance data was collected from 23133 girls from all districts in Malaysia. Analysis that used in this research was univariate clustering, discriminant analysis and machine learning. Univariate clustering helps in grouping the coordination. Next, discriminant analysis helps in validating the group and shows the prediction that correctly assigned to the group. Lastly, machine learning help in developing best fit model. In conclusion, this research can help many parties in an effort to improve coordination among girl aged 7 years old in Malaysia.