Physical Education Theory and Methodology (Sep 2019)

Pattern Recognition: Description of Modes of Teaching Boys Aged 7 Throwing a Small Ball at a Vertical Target

  • O. V. Ivashchenko,
  • M. O. Nosko,
  • Yu. M. Nosko,
  • S. O. Chernenko

DOI
https://doi.org/10.17309/tmfv.2019.3.04
Journal volume & issue
Vol. 19, no. 3
pp. 130 – 138

Abstract

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The study purpose was to determine the possibility of using pattern recognition methods to study the impact of physical exercises modes on teaching primary school children throwing a small ball at a vertical target. Materials and methods. The study participants were boys aged 7 years (n=48). The paper relied on analysis and generalization of data of scientific and methodological literature, general scientific methods of theoretical level, such as analogy, analysis, synthesis, abstraction, induction, as well as general scientific methods of empirical level: observation, testing, experiment. Results. The effectiveness of teaching boys aged 7 “throwing a ball at a vertical target” is positively influenced by 6-12 sets, 3 repetitions per set, rest interval of 60-180 seconds. The focus in choosing a teaching mode is on the number of repetitions per set. Conclusions. A full factorial experiment method makes it possible to mathematically describe the process in some local area of the factorial space and to verify the regression model. Regression equations provide an opportunity to select the modes of performing for each exercise being studied. Discriminant analysis has made it possible to determine the modes of physical exercises in the process of motor skills development; to answer the question as to how significantly the modes of training differ by the effectiveness of motor skills development; what motor tasks most substantially influence the differentiation of classes; what class the object belongs to based on the values of discriminant variables. To select the most rational mode of performing exercises in the process of motor skills development in boys aged 7, the first and second discriminant functions can be used, with a focus on the most informative variables.

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