运动员
大数据
数据科学
计算机科学
心理学
数据挖掘
医学
物理疗法
作者
Martin Mičiak,Dominika Tumová,Roman Adámik,Ema Kufová,Branislav Škulec,Nikola Mozolová,Aneta Hoferová
出处
期刊:Data
[Multidisciplinary Digital Publishing Institute]
日期:2025-06-24
卷期号:10 (7): 102-102
摘要
Education leads to building successful careers. However, different groups of students have different studying preferences. Our target group are athletes, combining their education and sports training. The main objective is to provide recommendations for an effective education system for athletes, improving their chances of finding new careers after leaving sports. Such a system must include Big Data and utilise AI possibilities currently available that support athletes’ career planning and development in a meaningful way. The main objective is specified by the following partial objectives: identifying what types of Big Data to analyse in connection with the athletes’ education; revealing what AI tools to include in the athletes’ education for their better preparation for a career after sports; determining what knowledge of AI and Big Data athletes need to stay relevant once they enter the labour market. Our study combines secondary and primary data sources. The secondary data (used in the orientation analysis) include case studies on AI and Big Data connected to education. The primary data were collected via a survey performed on over 200 Slovak junior athletes. The results show directions for the sports policymakers and sports organisations’ managers willing to improve their athletes’ career prospects.
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