Predicting Computer Science Ph.D. Completion

A Case Study

Bibliographic Data

ID20297014
AuthorsGlenda Cox (0000-0001-8185-0645, University of Alabama in Huntsville), Glenn W Cox, William E Hughes Jr, William E Hughes (0000-0002-7149-3987, U.S. Army Aviation and Missile Command), Letha H Etzkorn (0000-0003-3203-9236, University of Alabama in Huntsville), Mary Ellen Weisskopf (University of Alabama in Huntsville)
Year2009
Volume52
Issue1
Pages137-143
Publication date2009-02-01
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Education (JOURNAL)
Journal identifiersISSN: 0018-9359 • E-ISSN: 1557-9638
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/te.2008.921458
OpenAlexW2130613136
LanguageEN
Citations received3
References cited2

This paper presents the results of an analysis of indicators that can be used to predict whether a student will succeed in a Computer Science Ph.D. program. The analysis was conducted by studying the records of 75 students who have been in the Computer Science Ph.D. program of the University of Alabama in Huntsville. Seventy-seven variables were extracted from each student's record, and the variables were correlated with whether the student did or did not successfully graduate from the program. A multivariate model was developed that predicts success with a high degree of accuracy. Importantly, the model relies on variables that can be determined reasonably early in a student's Ph.D. class work, enabling its use as a selection metric. Hypotheses about the composition of the model are also presented and discussed

Class (philosophy) · Graduate students · Machine learning · Mathematics education · Metric (unit) · Multivariate analysis · Multivariate statistics · Pedagogy · Selection (genetic algorithm) · Artificial Intelligence · Computer Science · Engineering · Mathematics · Psychology · Software Engineering Research · Software Engineering Techniques and Practices

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