Predicting performance in an introductory computer science course
Communications of the ACM
The effect of high school computer science, gender, and work on success in college computer science
SIGCSE '89 Proceedings of the twentieth SIGCSE technical symposium on Computer science education
Predicting the success of freshmen in a computer science major
Communications of the ACM
The effect of student attributes on success in programming
Proceedings of the 6th annual conference on Innovation and technology in computer science education
The software engineering capstone: structure and tradeoffs
SIGCSE '02 Proceedings of the 33rd SIGCSE technical symposium on Computer science education
Validation of a model for predicting aptitude for introductory computing
SIGCSE '82 Proceedings of the thirteenth SIGCSE technical symposium on Computer science education
Mental models and programming aptitude
Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education
Abstraction ability as an indicator of success for learning computing science?
ICER '08 Proceedings of the Fourth international Workshop on Computing Education Research
Predicting student exam's scores by analyzing social network data
AMT'12 Proceedings of the 8th international conference on Active Media Technology
CS0 as an indicator of student risk for failure to complete a degree in computing
Journal of Computing Sciences in Colleges
Illustration of paradigm pluralism in computing education research
ACE '12 Proceedings of the Fourteenth Australasian Computing Education Conference - Volume 123
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Professors often develop anecdotal guidelines about how each student's past performance in their academic major relates to their performance in later courses. While these guidelines can be useful, a more formal statistical analysis of these relationships can provide valuable insight into predicted student performance, which can help professors guide their students to focus on potential areas of difficulty. In addition, such analyses can identify which courses are key indicators of later performance in the major. This additional insight into the relationships between the courses in the curriculum can help professors implement curriculum changes and measure the effects of those changes. In this paper, we present the results of such an analysis for computer science majors at the U.S. Air Force Academy.