The effects of individual differences on CS2 course performance across universities
Abstract
Research is presented that examined the effects of various measures of prior computer science experience and cognitive abilities on overall performance in a CS2 course. Participants selected from the CS2 course at two southeastern state universities were used within this study, resulting in a sample size of 161 (School A, n = 76; School B, n = 85). School A is a mid-sized comprehensive university and School B is a large research-intensive university.Self-reported data were collected on measures of experience in object-oriented processing, UNIX programming, web design, computing platforms, and various CS experience. Further, cognitive abilities measures of spatial orientation, visualization, logical reasoning, and flexibility were administered.The results show that the schools significantly differed on all measures of cognitive ability and most measures of prior computer science experience. The schools also differed on the extent to which these measures were related to overall course performance. The results suggest that, for school A, the cognitive ability visualization and the prior computer science experience measure of OO processing were significantly related to course performance. However, when examining school B, no measures were found significant.
References
[1]
ACM Computing Curricula. (2001). Computing curricula 2001 final report. Joint Task Force on Computing Curricula -- IEEE and ACM.
[2]
Alspaugh, C.A. (1972) Identification of some components of computer programming aptitude. Journal of Research in Mathematics Education, 3, 89--98.
[3]
Bishop-Clark, C. (1998). An undergraduate course in Object-Oriented Software Design. Proceedings from Frontiers in Education Conference '98. Tempe, AZ.
[4]
Carroll, J.B. (1974). Psychometric tests as cognitive tasks: A new structure of intellect (p. 74--16). Princeton, NJ: Educational Testing Service.
[5]
Denelsky, G.Y. & McKee, M.G. (1974). Prediction of computer programmer training and job performance using the AABP test. Personal Psychology, 129--137.
[6]
Mazlack, L J. (1980). Identifying potential to acquire programming skill, Communications of the ACM, 23(1), 14--17.
[7]
Deckro, R.F. & Woundenberg, H.W. (1977). MBA admission criteria and academic success, Decision Sciences, 765--799.
[8]
Ekstrom, R. B., French, J. W., & Harman, H. H. (1979). Cognitive factors: Their identification and replication. Multivariate Behavioral Research Monographs, 79(2).
[9]
Evans, G E. & Simkins, M G. (1989). What best predicts computer proficiency?, Communications of the ACM, 32(11), 1322--1327.
[10]
Goold, A., & Rimmer, R. (2000). Factors affecting performance in first-year computing, ACM SIGCSE Bulletin, 32(2), 39--43.
[11]
Glorfeld, L. W., & Fowler, G. C. (1982). Validation of a model for predicting aptitude for introductory computing. Association for Computing Machinery Special Interest Group Computer Science Education Bulletin, 14(1), 140--143.
[12]
Katz, S., Aronis, J.D., Allbritton, C. Wilson, C & Soffa, M.L., (2003). An experiment to identify predictors of achievement in an introductory computer science course. ACM Conference on Computer Personnel Research.
[13]
Jakiela, M., & Fayad, L. (1989). Identification of factors that contribute to engineering design skill. Transactions of the IEEE.
[14]
Morrison, M. & Newman, T. S. (2001). A study of the impact of student background and preparedness on outcomes in CS I, Proceedings from SIGCSE 2001, Charlotte, NC,179--183.
[15]
Petersen, C.C., & Howe, T.G. (1979). Predicting academic success in introduction to computers. Association of Educational Data Systems Journal, 182--191.
[16]
Rountree, N., Rountree, J. & Robins, A.V. (2002). Predictors of success and failure in a CS1 course. Special Interest Group on Computer Science Education Bulletin, 34(4):121--124.
[17]
Stevens, L.J., Wileman, S., & Konvalina, J. (1981). Group differences in computer aptitude. Association of Educational Data Systems Journal, 84--95.
[18]
Ventura, P. (2004). Unpublished Dissertation: On the origins of programmers: Identifying predictors of success for an objects-first CS1. Computer Science, University at Buffalo, SUNY.
[19]
Werth, L. (1989). Predicting student performance in a beginning computer science class, Proceedings of the 17th SIGCSE Technical Symposium on Computer Science Education, Cincinnati, Ohio.
[20]
Wilson, B. (2000). Contributing factors to success in computer science: A study of gender differences. Unpublished dissertation from the Department of Curriculum and Instruction, Southern Illinois University at Carbondale.
[21]
Witkin, H., Moore, C., Goodenough, D., & Cox, P. (1977). Field-dependent and field-independent cognitive styles and their educational implications. Review of Educational Research, 47, 1--64.
[22]
Witkin, H., Oltman, P., Raskin, E., & Karp, S. (1971). A manual for the embedded figures test. Palo Alto, CA: Consulting Psychologists Press.
Index Terms
- The effects of individual differences on CS2 course performance across universities
Recommendations
The effects of individual differences on CS2 course performance across universities
SIGCSE '05: Proceedings of the 36th SIGCSE technical symposium on Computer science educationResearch is presented that examined the effects of various measures of prior computer science experience and cognitive abilities on overall performance in a CS2 course. Participants selected from the CS2 course at two southeastern state universities ...
Investigating the Role of Different Prep Pathways on CS2 Performance Across Three Different Majors
SIGITE '21: Proceedings of the 22nd Annual Conference on Information Technology EducationResearch have shown that introductory programming sequence have a significant impact in the retention of students in computing and engineering majors. There has been extensive research about the CS1 course [1]. Much less has been written about the CS1.5 ...
The year in review: changes and lessons learned in the design and implementation of the AP CS exam in Java
SIGCSE '05: Proceedings of the 36th SIGCSE technical symposium on Computer science educationThe 2004 school year marked significant changes in the Advanced Placement (AP) Computer Science curriculum. Most visibly, Java replaced C++ as the exam-testable programming language. More fundamentally, however, the AP CS curriculum now places a greater ...
Comments
Information & Contributors
Information
Published In
February 2005610 pagesISBN:1581139977DOI:10.1145/1047344- Conference Chairs:
- Wanda Dann,
- Tom Naps,
- Program Chairs:
- Paul Tymann,
- Doug Baldwin
Copyright © 2005 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 February 2005
Published in SIGCSE Volume 37, Issue 1
Check for updates
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations4Total Citations
- 393Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Oct 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in