JOURNAL ARTICLE

Learning Model for Game-Based Long Jump Skills In Children Aged 12-14 Years.

  • Published In: Cuestiones de Fisioterapia, 2025, v. 54, n. 3. P. 5046 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chong, You; Samsudin; Wasan, Aan 3 of 3

Abstract

This research uses qualitative and quantitative approaches to find answers to existing problem formulations. This research entitled game-based long jump learning model uses research and development steps from Borg and Gall which consist of 10 steps of the Borg & Gall development research model was taken into consideration, As it is known that the minimum value of the experimental group's long jump pre test is 57 while the maximum value is 67 with an average value of 61.93 and a standard deviation of 2.96 Then it can be seen that the minimum value of the experimental group's long jump post test is 99 while the maximum value is 113 with an average of 105.73 and a standard deviation of 4.06 Based on the results of needs analysis, expert validation, field trials, effectiveness tests and discussion of research results on this game-based long jump skills learning model product, it can be concluded that: 1. This series of research processes has produced 39 product models for learning game-based long jump skills for children aged 12-14 years which are packaged in the form of textbooks. 2. A game-based learning model for long jump skills has been proven to be effective in improving long jump skills in children aged 12-14 years. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Cuestiones de Fisioterapia. 2025/09, Vol. 54, Issue 3, p5046
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2025
  • ISSN:1135-8599
  • Accession Number:186683762
  • Copyright Statement:Copyright of Cuestiones de Fisioterapia is the property of Cuestiones de Fisioterapia and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.