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Abstract:
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Undergraduate students with data science and modeling prerequisites take an elective upper-level data science course centered on "competitions" (sports, eSports, board games, elections). Under the constraints of a hybrid learning environment, students build connections with their peers in a project incorporating seemingly disparate learning objectives via Qwixx, a game of chance and partial information that can be learned in 5 to 10 minutes yet complicated enough that many valid strategies exist. Over several weeks, they build an Excel spreadsheet to play with each other over Zoom, program the game in R, and build strategies in teams to compete in a round robin head-to-head simulation in R. Along the way, they develop algorithmic thinking, expand their view of Monte Carlo simulations, exercise the software development life cycle, collaborate and practice reproducibility in RStudio and GitHub (Classroom), strategize and make decisions under uncertainty, and ultimately create their own paired comparisons data to analyze. We will also examine how student performance in this intensive enterprise is associated with other student learning in this course.
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