Analyzing Factors Affecting Student Anxiety Using K-Means Clustering and Fuzzy Best-Worst Method

One of the most difficult challenges that students experience is the anxiety that they struggle with during their studies. Various factors affect or are affected by students' anxiety. Things like depression and self-esteem affect students' anxiety, and the level of anxiety also affects them. Also, students' health and their level of success during their studies are affected by their anxiety. In this article, the effects of anxiety on students' lives are first examined using visualization techniques. The effects of self-confidence and depression factors on students' anxiety are then analyzed using statistical approaches. Subsequently, a clustering analysis is performed using an unsupervised K-means machine learning approach. Based on the elbow method and silhouette score methods, an optimal number of k is determined to be five. These five clusters are students with different levels of self-esteem, depression, and anxiety. Then, the importance of external factors and subfactors affecting students' anxiety was investigated. This process was done using the fuzzy best-worst method. The most important category of factors is the environmental category environmental category with a weight of 41.5%. Also, the most significant factor is basic needs with global weight of 13%.