Title: Tennis-assisted teaching assessment technology based on machine learning algorithm

Authors: Miaomiao Li

Addresses: College of Physical Education, Anyang Preschool Education College, Anyang, Henan 456150, China

Abstract: To excel in table tennis, players must understand both their strengths and weaknesses to develop effective strategies and improve their chances of winning. Previous studies were often limited by incomplete or inaccurate data. To address this, we developed "The Intellectual Tactical System in Competitive Table Tennis", which utilises video analysis of competition matches to collect comprehensive data. We proposed a machine learning approach that combines feature selection with association rules to extract meaningful patterns. This study used matches featuring Yun-Ju Lin as case examples, employing the 3S theory - speed, spin, and spot - for data collection and analysis. By identifying key factors and match contexts, winning strategy models were constructed. The findings can assist Yun-Ju Lin in optimising his training and tactical planning. This approach may also serve as a valuable framework for elite players and coaches aiming to conduct in-depth strategic analysis.

Keywords: tennis-assisted; machine learning; VR; virtual reality; teaching assessment; sports education technology; data-driven assessment; smart education; human motion analysis.

DOI: 10.1504/IJNVO.2025.151502

International Journal of Networking and Virtual Organisations, 2025 Vol.33 No.3, pp.187 - 207

Received: 16 Mar 2024
Accepted: 27 Aug 2024
Published online: 03 Feb 2026 *

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