大專院校學生體育課程根據體適能強度分群之研究－以明新科技大學為例

過去研究多著重在利用學院、科系、年級或身體質量指數進行分群，並觀察分群後每群體能表現來設計體育課程，以供學生選修。但這些分群結果通常群間獨立性不佳，以此分群使所設計出的體育課針對性不足，造成訓練效果不到設計時的預期。本研究以學院及身體質量指數作為分群標的，並依據分析結果討論其設計運動課程的合適性。再使用K-means分群法輔以平均測影法進行分群，將三種不同的分群方法加以比較。分析結果顯示，利用學院做為分群標的的分群方法，其群間獨立性弱，並不適合作為體育課程設計之依據；利用身體質量指數作為分群標的的分群方法，其群間獨立性稍佳，但做為體育課程設計之依據可能會設計出較多重覆内容，且課程針對性不足，故不太適合做為設計之依據；K-means分群法輔以平均側影法進行分群結果，群間獨立性最佳，且分類法較其他方法更具有彈性與靈活度，所設計出之體育課程可以更具有針對性，可以期待有較好的訓練效果。

關鍵字

身體質量指數； K-means ；平均測影法；分群

並列摘要

On the design of sport classes, previous studies used to cluster by college, department, grade level, or body mass index. After clustering, the physical performance of each group could be observed to design the course content specifically for students to take. However, these results of clustering usually lack of individual independence between groups. Sport classes designed according to these results with poor appropriateness could cause less training effect than expected. In this research, we analyze and discuss the suitability of the design of sport classes which uses colleges and body mass index as clustering rules. Then we use the K-means clustering method supplemented with Average Silhouette Method to cluster, and compare the three different clustering methods. In conclusion, firstly, it is not suitable for the design of sport classes to clustering by college, due to its weak individual independence between groups. Secondly, using body mass index as the clustering rule results in better individual independence between groups, yet it may turn out more repetitive course contents and less appropriateness. Last but not least, K-means clustering method supplemented with Average silhouette Method can results in the best individual independence between groups and has more flexibility than other methods. Based on which, we can look forward to better training effect.