A Nearest Neighbors Analysis of Student Academic Performance in Computer Science (Abstract Only)

Students increasingly decide to go to college in order to get better jobs and make more money. However, these advantages are typically thwarted if a student fails to graduate. Although much research has been aimed at predicting college performance using data collected before entering college, this preliminary work focuses on how college-level data could be used to inform student decision making. This work acquired historical class grades, test scores, and degree information for all students who have taken any computer science classes at Appalachian State University. This poster presents a web application that allows users to explore these data by selecting a target activity such as a class, and filtering students based on test scores, degrees, or how they have performed in other classes. The application displays overlaid histograms comparing how students in the subset perform relative to the class as a whole. For example, when a student considers retaking a course they may find it useful to know how other students with similar grades have performed in the major. For example, among the 29 attempts by 22 students with a 'C' in discrete math and CS 1, only 10 earned the required 'C' in CS 2 (35%) and 12 failed the course (41%). Three of these students went on to graduate with a degree in computer science (14%) and six in computer information systems (27%) while five did not graduate from Appalachian (23%).