Predicting student performance in a beginning computer science class
SIGCSE '86 Proceedings of the seventeenth SIGCSE technical symposium on Computer science education
What best predicts computer proficiency?
Communications of the ACM
Proceedings of the thirty-first SIGCSE technical symposium on Computer science education
Does it help to have some programming experience before beginning a computing degree program?
Proceedings of the 5th annual SIGCSE/SIGCUE ITiCSEconference on Innovation and technology in computer science education
Factors affecting performance in first-year computing
ACM SIGCSE Bulletin
Predicting the success of freshmen in a computer science major
Communications of the ACM
Contributing to success in an introductory computer science course: a study of twelve factors
Proceedings of the thirty-second 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
Predicting student success in an introductory programming course
ACM SIGCSE Bulletin
Predicting success in a first programming course
SIGCSE '82 Proceedings of the thirteenth SIGCSE technical symposium on Computer science education
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Predictors of success and failure in a CS1 course
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Examining the role of self-regulated learning on introductory programming performance
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Proceedings of the 39th SIGCSE technical symposium on Computer science education
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Performance and progression of first year ICT students
ACE '08 Proceedings of the tenth conference on Australasian computing education - Volume 78
Abstraction ability as an indicator of success for learning computing science?
ICER '08 Proceedings of the Fourth international Workshop on Computing Education Research
Coarse-grained detection of student frustration in an introductory programming course
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The learning context: Influence on learning to program
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BlueJ Visual Debugger for Learning the Execution of Object-Oriented Programs?
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Proceedings of the Twelfth Australasian Conference on Computing Education - Volume 103
An exploration of internal factors influencing student learning of programming
ACE '09 Proceedings of the Eleventh Australasian Conference on Computing Education - Volume 95
Evaluating the use of learning objects in CS1
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Proceedings of the seventh international workshop on Computing education research
Consideration of human factors for prioritizing test cases for the software system test
EPCE'11 Proceedings of the 9th international conference on Engineering psychology and cognitive ergonomics
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A student perspective on prior experience in CS1
Proceeding of the 44th ACM technical symposium on Computer science education
A methodology for teaching programming for beginners
Proceedings of the ninth annual international ACM conference on International computing education research
New CS1 pedagogies and curriculum, the same success factors?
Proceedings of the 45th ACM technical symposium on Computer science education
No tests required: comparing traditional and dynamic predictors of programming success
Proceedings of the 45th ACM technical symposium on Computer science education
Design and benefits of an on-site tutoring program for the first programming class
Journal of Computing Sciences in Colleges
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This paper documents a study, carried out in the academic year 2003-2004, on fifteen factors that may influence performance on a first year object-oriented programming module. The factors included prior academic experience, prior computer experience, self-perception of programming performance and comfort level on the module and specific cognitive skills. The study found that a student's perception of their understanding of the module had the strongest correlation with programming performance, r=0.76, p‹0.01. In addition, Leaving Certificate (LC) mathematics and science scores were shown to have a strong correlation with performance. A regression module, based upon a student's perception of their understanding of the module, gender, LC mathematics score and comfort level was able to account for 79% of the variance in programming performance results.