Understanding health and behavioral trends of successful students through machine learning models

TL;DR

This study uses machine learning to analyze how physical, mental, lifestyle, and personality factors influence college students' academic performance over a semester, highlighting the importance of behavioral patterns and time spent locations.

Contribution

It introduces a mutual agreement method focusing on model parameters to identify behavioral trends related to academic success, emphasizing the significance of lifestyle and personality factors.

Findings

Time spent locations strongly correlates with GPA.

Lifestyle and personality factors are more influential than mental and physical health.

Behavioral patterns vary across different periods of the semester.

Abstract

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.