Future anxiety is the worry and concern individuals experience regarding uncertainties and potential negative outcomes in their future. This emotional state can manifest at different stages of students’ academic lives and can impact their academic performance and social relationships. In the process of coping with negative experiences and overcoming challenges, psychological resilience plays a crucial role. Students who struggle to manage stress and have high levels of anxiety tend to experience future anxiety more intensely. The aim of this study is to determine the mediating role of subjective well-being in the relationship between psychological resilience and future anxiety among university students. The study was conducted with a total of 483 university students, including 280 females and 203 males. Data were collected using the Connor–Davidson Resilience Scale (CD-RISC-10), Subjective Well-Being Scale (SWB-7), and Future Anxiety (Dark Future) Scale (Short Form). Analyses were performed using AMOS 22.0 and SPSS 27.0 software. The findings indicate that psychological resilience has a significant negative effect on future anxiety, a significant positive effect on subjective well-being, and that subjective well-being has a significant negative effect on future anxiety. Additionally, the study found that subjective well-being mediates the relationship between psychological resilience and future anxiety.

The purpose of this research was to determine classification accuracy of the factors affecting the success of students' reading skills based on PISA 2018 data by using Artificial Neural Networks, Decision Trees, K-Nearest Neighbor, and Naive Bayes data mining classification methods and to examine the general characteristics of success groups. In the research, 6890 student surveys of PISA 2018 were used. Firstly, missing data were examined and completed. Secondly, 24 index variables thought to affect the success of students' reading skills were determined by examining the related literature, PISA 2018 Technical Report, and PISA 2018 data. Thirdly, considering the sub-classification problem, the students were scaled in two categories as “Successful” and “Unsuccessful” according to the scores of PISA 2018 reading skills achievement test. Statistical analysis was conducted with SPSS MODELER program. At the end of the research, it was determined that Decision Trees C5.0 algorithm had the highest classification rate with 89.6%, the QUEST algorithm had the lowest classification rate with 75%, and four clusters were obtained proportionally close to each other in Two-Step Clustering analysis method to examine the general characteristics according to the success scores. It can be said that the data sets are suitable for clustering since the Silhouette Coefficient, which is calculated as 0.1 in clustering analyses, is greater than 0. It can be concluded that according to achievement scores, all data mining methods can be used to classify students since these models make accurate classification beyond chance.