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Date: Monday October 23, 2023
Time: 5:30pm MDT
Room: Zoom uidaho.zoom.us, Meeting ID 881 0237 3671, passcode UICS501
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Abstract: A Monte Carlo algorithm is designed to predict the average time to graduate by enrolling virtual students in a degree plan. The algorithm can be used to improve graduation rates by identifying bottlenecks in a degree plan (e.g. low pass rate courses and prerequisites). Random numbers are used to determine whether students pass or fail classes by comparing them to institutional pass rates. Courses cannot be taken unless prerequisites and corequisites are satisfied. The output of the algorithm generates a relative frequency distribution which plots the number of students who graduate by semesters. Pass rates of courses can be changed to determine the courses that have the greatest impact on time to graduate. Prerequisites can also be removed to determine whether certain prerequisites significantly affect the time to graduate.
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Dr. David Torres received his PhD in Mathematics from the University of New Mexico. Since then he has worked at Los Alamos National Laboratory as a technical staff member in computational fluid dynamics applied to internal combustion engines. He is currently an Associate Professor at Northern New Mexico College in the Mathematics and Physical Science Department. His research interests include computational fluid dynamics, parallel computing, biological models (T cells and honey bees), and Monte Carlo methods.