Bayesian network analysis of computer science grade distributions

Abstract

Time to completion is a major factor in determining the total cost of a college degree. In an effort to reduce the number of students taking more than four years to complete a degree, we propose the use of Bayesian networks to predict student grades, given past performance in prerequisite courses. This is an intuitive approach because the necessary structure of any Bayesian network must be a directed acyclic graph, which is also the case for prerequisite graphs. We demonstrate that building a Bayesian network directly from the prerequisite graph results in effective predictions, and demonstrate a few applications of the resulting network in areas of identifying struggling students and deciding upon which courses a department should allocate tutoring resources. We find that many of our observations agree with what has long been considered conventional wisdom in computer science education.

An English-Language Argumentation Interface for Explanation Generation with Markov Decision Processes in the Domain of Academic Advising

“…Our research group is also developing a system to automatically generate complete academic advising domains that capture all classes in a university [Guerin and Goldsmith 2011]. The long term goal of this ongoing research project is to develop an end-to-end system to aid academic advisors that builds probabilistic grade predictors (e.g., Anthony and Raney [2012]), models student preferences, plans, and explains the offered recommendations.…”

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“…That means each item is tested over the whole data. Graph based analysis helps in finding out the cause-effect of the situations which is used by a few researchers in their study [8][9] [11]. In the study, regression is used to analyze the performance.…”

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Artificial Student Agents and Course Mastery Tracking

“…The study of student modeling and their potential usage continues to be an active area of research. For more on these topics, see (Anthony and Raney, 2012;Baker et al, 2008;Brusilovsky et al, 2005;Carmona et al, 2008;Conati et al, 2002;Garcia et al, 2007;Gardner and Belland, 2012;Gordillo et al, 2013;Kobsa, 2007;Li et al, 2011;Pardos and Heernan, 2010;Soliman and Guetl, 2013;Vomlel, 2004).…”