逢甲大學廁所與資源回收區滿意度調查

Abstract

摘要 本研究使用線上發放問卷蒐集而得的資料進行逢甲大學廁所與資源回收區滿意度之研究，並以逢甲大學六棟主要教學與活動大樓（商學大樓、資電大樓、人言大樓、圖書館、語文大樓與忠勤大樓）為研究對象，針對「廁所」與「資源回收區」兩項公共設施進行滿意度調查。問卷初始透過R語言建置之Shinyapp平台發放，後續再以Google表單補充蒐集，調查面向涵蓋整潔度、設施完備性、隱私與安全性、異味程度等，以量化學生的使用感受，掌握校園公共空間之實際現況。研究方法包含資料處理與變數縮減，並以性別作為分群基準，透過R程式進行描述性統計及多變量分析，包含主成分分析、群集分析與親緣樹圖，藉以探討各變項間的相關性、變異性以及學生群體的異質性。分析結果顯示，人言大樓在廁所與資源回收區的綜合評價表現最佳，忠勤大樓則為最低；此外，商學大樓的資源回收區在各大樓分區比較中得分最低，甚至低於忠勤大樓，顯示學生對該設施的不滿意情形尤為突出。本研究不僅反映不同大樓公共設施在學生體驗上的差異，更揭示使用R程式進行多變量分析、問卷設計、資料處理與視覺化圖表的可行性。研究成果可提供校方作為後續修繕與優化的重要依據，期望進一步提升校園生活品質並營造更友善的學習環境。

Abstract This study investigates student satisfaction with restrooms and recycling areas at Feng Chia University, focusing on six major academic and activity buildings: the Business Building, the Information and Electrical Engineering Building, the Humanities Building, the Library, the Language Building, and the Zhongqin Building. An online questionnaire was initially distributed through a Shinyapp platform built in R, followed by additional data collection via Google Forms. The survey covered multiple dimensions, including cleanliness, facility completeness, privacy and safety, and odor levels, in order to quantify students’ perceptions and capture the actual conditions of campus public spaces. The research methodology involved data processing and variable reduction, with gender used as the basis for grouping. Statistical analyses were conducted in R, including descriptive statistics and multivariate techniques such as principal component analysis, cluster analysis, and phylogenetic tree visualization, to explore correlations, variability, and heterogeneity among students. The results showed that the Humanities Building received the highest overall satisfaction ratings for both restrooms and recycling areas, while the Zhongqin Building scored the lowest. Moreover, the recycling area of the Business Building was rated the lowest among all facilities, even below that of the Zhongqin Building, indicating particularly notable dissatisfaction. This study not only reflects the differences in student experiences across buildings but also demonstrates the feasibility of applying R for multivariate analysis, questionnaire design, data processing, and visualization. The findings provide valuable insights for the university to guide facility renovations and optimization, with the ultimate goal of improving campus life quality and fostering a more student-friendly learning environment.