Title :
Integrated STEM learning within health science, mathematics and computer science
Author :
Droppa, Marjorie ; Wei Lu ; Bemis, Shari ; Ocker, Liette ; Miller, Mark
Author_Institution :
Dept. of Health Sci., Keene State Coll., Keene, NH, USA
Abstract :
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).
Keywords :
behavioural sciences computing; data analysis; educational administrative data processing; educational institutions; further education; health care; learning (artificial intelligence); mathematics; Mathematics; Science-Technology-Engineering-and-Mathematics; academic achievement; advanced machine learning analysis techniques; computer science; data collection tool; exploratory factor analysis; health science; innovative analysis methods; integrated STEM learning module; integrated educational activities; interdisciplinary learning experience; north-central university; risky wellness behaviors; Big data; Clustering algorithms; Computer science; Drugs; Education; STEM; academic achievement; machine learning techniques; risky wellness behavior;
Conference_Titel :
Integrated STEM Education Conference (ISEC), 2015 IEEE
Conference_Location :
Princeton, NJ
Print_ISBN :
978-1-4799-1828-7
DOI :
10.1109/ISECon.2015.7119932
