Integrated STEM learning within health science, mathematics and computer science

In this integrated STEM learning module we developed a data collection tool and used innovative analysis methods to investigate the relationship between academic achievement and risky wellness behaviors among college students. Exploratory factor analysis (EFA) was performed using data from college students (n = 1,499) at a large north-central university. Advanced machine learning analysis techniques found a strong connection between student wellness behavior and academic achievement and that this relationship can be predicted using wellness behavior data. The real world research project in this study integrated educational activities among Mathematics, Computer Science, and Health Science creating an interdisciplinary learning experience within Science, Technology, Engineering and Mathematics (STEM).