Clusters and factors influencing student satisfaction among graduates of Turku university of applied sciences

Student satisfaction in studies is one of the most critical areas of information for universities, often highlighted through student questionnaires. Understanding why some students are more satisfied with their studies than others is essential for improving organizational performance. These aspects have been under scrutiny at Turku University of Applied Sciences (TUAS) in earlier studies. TUAS has previously investigated the attributes influencing student satisfaction and the correlation between staff satisfaction and student satisfaction. While some factors explaining variations in satisfaction are straightforward, others are more complex and potentially critical for the institution’s future. Our prior research demonstrated that student satisfaction correlates with staff perceptions of justice. Additionally, we identified several variables influencing student satisfaction. Student satisfaction is particularly intriguing because it is often associated with positive outcomes, such as higher graduation rates. Given that universities of applied sciences aim to produce new professionals for society, understanding the variables impacting student graduation remains a priority. In previous research, we studied the correlation between student satisfaction and on-time graduation. Interestingly, no significant correlation was found. This finding motivated further exploration into student satisfaction, focusing on identifying clusters and factors in the data. This study utilized data from the AVOP feedback questionnaire, which collects evaluations and feedback from graduating students about their education. The AVOP findings are used locally by institutions to enhance processes and nationally for performance-based monitoring and funding decisions. All graduating degree students from Finnish universities of applied sciences are invited to complete the questionnaire. The dataset comprised responses from 1 814 TUAS graduates who completed the AVOP questionnaire in 2023. The data analysis was conducted using Excel and Python. Samples were extracted from the dataset and incorporated into a statistical model using Excel spreadsheets. This study aimed to answer the following research questions: Is there a correlation between student satisfaction and specific factors at the time of graduation? Can students be grouped into clusters based on the data? How can these correlations and clusters be interpreted, and what conclusions can be drawn? The findings provide valuable insights for understanding the dynamics of student satisfaction. Future research directions and practical recommendations will also be presented in the paper.