Sexual violence and intimate partner violence have garnered national attention during the past few years. Although college-aged women are highly vulnerable to violence, there is little research on the relationship between violence and academic progress.
Xiaoqian Liu and Colleen Sanders proposed a comprehensive way to measure academic performance by collecting data from emergency rooms, a student enrollment system and a course evaluation website. In addition to traditional regression and classification methods, they applied distance metric learning methods to discovering a set of evaluation metrics of academic progress and testing hypotheses on the impact of abuse history and health care utilization.
Furthermore, Liu and Sanders aimed to automatically extract geo-temporal information relevant with sexual assaults from text documents, such as online news reports and emergency records. Findings will assist the development of evidence-based policies to promote the physical, psychological and social health of victims and foster their academic progress.
Xiaoqian Liu is a second-year MS student in the Department of Systems and Information Engineering. Her research interests are machine learning and text mining.
Colleen Sanders is a PhD student in the School of Nursing.