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Using Machine Learning And Traditional Statistics To Explore Retention And Knowledge Structure In Stem With An Emphasis On Physics, Cabot Alexander Zabriskie
Using Machine Learning And Traditional Statistics To Explore Retention And Knowledge Structure In Stem With An Emphasis On Physics, Cabot Alexander Zabriskie
Graduate Theses, Dissertations, and Problem Reports
Retention of Science, Technology, Engineering, and Mathematics (STEM) students is a serious problem as STEM graduation rates continue to lag the growing demand for the skills taught by these degree programs. Critical to fixing this “leaky pipeline” is investment in improving retention in the first two years of college study and increasing and maintaining the interest of K-12 students in STEM. This thesis will address this in three parts. The first is through evaluation of conceptual tests used to evaluate course improvements to determine the structure student knowledge measured by them. The second part uses machine learning to construct early …
Persistence Of Undergraduate Women In Stem Through Timsi, Heather L. Henderson
Persistence Of Undergraduate Women In Stem Through Timsi, Heather L. Henderson
Graduate Theses, Dissertations, and Problem Reports
There is a need to increase the persistence of women in science careers. This study focuses on undergraduate women majoring in or interested in science. Despite interest early on in their academic careers, women are leaving STEM at higher rates than other fields. Through the framework of the Tripartite Integration Model of Social Influence (TIMSI), this study explores psychosocial factors leading to integration in science careers. 484 undergraduate women from 9 universities in the Colorado/Wyoming Front Range and the Carolinas were recruited into this study and surveyed through 8 waves of data collection. A model building process was performed using …