The psychology of computer programming
The psychology of computer programming
Predicting performance of programmer trainees in a post-high school setting
SIGCPR '71 Proceedings of the ninth annual SIGCPR conference
SIGCSE '86 Proceedings of the seventeenth SIGCSE technical symposium on Computer science education
Predicting student performance in a beginning computer science class
SIGCSE '86 Proceedings of the seventeenth SIGCSE technical symposium on Computer science education
Laying the foundations for computer science
SIGCSE '89 Proceedings of the twentieth SIGCSE technical symposium on Computer science education
The 1988–89 Taulbee survey report
Communications of the ACM
Predicting success of a beginning computer course using logistic regression (abstract only)
CSC '87 Proceedings of the 15th annual conference on Computer Science
Psychological differences in university computer student populations
SIGCSE '85 Proceedings of the sixteenth SIGCSE technical symposium on Computer science education
The effect of student attributes on success in programming
Proceedings of the 6th annual conference on Innovation and technology in computer science education
Programming: factors that influence success
Proceedings of the 36th SIGCSE technical symposium on Computer science education
Assessing long-term student performance in programming subjects
Journal of Computing Sciences in Colleges
Predicting student exam's scores by analyzing social network data
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Remediation and student success in CIS programs
Proceedings of the 45th ACM technical symposium on Computer science education
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This paper examines to what extent a student's aptitude in computer programming may be predicted through measuring certain cognitive skills, personality traits and past academic achievement. The primary purpose of this study was to build a practical and reliable model for predicting success in programming, with hopes of better counseling students. Results from correlating predictor variables with a student's final numerical score confirmed past studies which showed the diagramming and reasoning tests of the Computer Programmer Aptitude Battery and a student's GPA to be the predictors most closely associated with success. A multiple regression equation developed from 5 predictors correctly classified 61 of 79 students (77.2%) into low and high aptitude groups.