New Orleans, Louisiana
June 26, 2016
June 26, 2016
June 29, 2016
978-0-692-68565-5
2153-5965
Exploring Student Affairs, Identities, and the Professional Persona
Liberal Education/Engineering & Society
20
10.18260/p.26545
https://peer.asee.org/26545
1914
Sherif is a PhD candidate at the Department of Computer Science, Virginia Polytechnic Institute and State University and is a graduate research assistant at Network Dynamics and Simulations Science Laboratory. Sherif’s research work lies at the intersection of computation, biology and education: in particular, he is interested in designing and building software systems to enable domain experts to easily access and effectively use high performance computing to perform and share the findings of simulations and large scale data analyses. Other aspects of his research focus on how to use these systems as learning tools for students and teachers.
Chris is a Research Scientist at the Biocomplexity Institute at Virginia Tech. His research interests include discrete dynamical systems, agent-based modeling and simulation, distributed and high performance computing, algorithms, social sciences and modeling, and network science.
Madhav Marathe is the director of the Network Dynamics and
Simulation Science Laboratory and professor in the department of
computer science, Virginia Tech.
His research interests are in computational epidemiology,
network science, design and analysis of algorithms,
computational complexity, communication networks and high performance computing.
Before coming to Virginia Tech, he was a Team Leader
in the Computer and Computational Sciences division at the Los Alamos National
Laboratory (LANL) where he led the basic research programs in
foundations of computing and high performance simulation science for
analyzing extremely large socio-technical and critical infrastructure
systems. He is a Fellow of the IEEE, ACM and AAAS.
Ravi received his Ph.D. in Computer Science in 1984 and joined the Computer Science
faculty at the University at Albany -- State University of New York. His current title is
DIstinguished Teaching Professor. His areas of interest include algorithms, discrete dynamical
systems, data mining, network science and wireless networks.
Kenneth Reid is the Assistant Department Head for Undergraduate Programs in Engineering Education at Virginia Tech. He is active in engineering within K-12, serving on the TSA Board of Directors. He and his coauthors were awarded the William Elgin Wickenden award for 2014, recognizing the best paper in the Journal of Engineering Education. He was awarded an IEEE-USA Professional Achievement Award in 2013 for designing the nation's first BS degree in Engineering Education. He was named NETI Faculty Fellow for 2013-2014, and the Herbert F. Alter Chair of Engineering (Ohio Northern University) in 2010. His research interests include success in first-year engineering, engineering in K-12, introducing entrepreneurship into engineering, and international service and engineering. He has written two texts in Digital Electronics, including the text used by Project Lead the Way.
In the U.S., major depressive disorder affects approximately 14.8 million American adults. Furthermore, depression can lead to a several other illnesses and disabilities. Economic burden of depression is estimated to be $53 billion annually in the U.S. alone. Depression can reach high levels that can lead to suicide, the third leading cause of death among the U.S. college-aged population.
Studies show a direct relation between mental health and academic success. In particular, depression is a significant predictor of lower GPA and increased drop out rate. A 15 point increase on the depression scale correlates with a 0.17 drop in GPA and corresponds to a 4.7 percent increase in probability of dropping out. High dropout rates also adversely impact both universities and society.
In this work, we construct and exercise an agent-based model (ABM) of the evolution of depression among a population of roughly 19,000 college students. This model includes within-agent interactions among depression symptoms and agent-to-agent interactions defined by a college student social network. We conduct simulation studies to identify (model) parameters and initial conditions that most influence population outcomes. Connectivity among within-agent symptoms is demonstrated to have a large effect on population levels of depression.
Elmeligy Abdelhamid, S., & Kuhlman, C. J., & Marathe, M. V., & Ravi, S. S., & Reid, K. (2016, June), Agent-Based Modeling and Simulation of Depression and Its Impact on Student Success and Academic Retention Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26545
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