  This study analyzes patterns of physical, mental, lifestyle, and personality
factors in college students in different periods over the course of a semester
and models their relationships with students' academic performance. The data
analyzed was collected through smartphones and Fitbit. The use of machine
learning models derived from the gathered data was employed to observe the
extent of students' behavior associated with their GPA, lifestyle, physical
health, mental health, and personality attributes. A mutual agreement method
was used in which rather than looking at the accuracy of results, the model
parameters and weights of features were used to find common behavioral trends.
From the results of the model creation, it was determined that the most
significant indicator of academic success defined as a higher GPA, was the
places a student spent their time. Lifestyle and personality factors were
deemed more significant than mental and physical factors. This study will
provide insight into the impact of different factors and the timing of those
factors on students' academic performance.
