Predicting student performance in a beginning computer science class

ABSTRACT

This study investigated the relationship between the student's grade in a beginning computer science course and their sex, age, high school and college academic performance, number of mathematics courses, and work experience. Standard measures of cognitive development, cognitive style, and personality factors were also given to 58 students in three sections of the beginning Pascal programming class. Significant relationships were found between the letter grade and the students' college grades, the number of hours worked and the number of high school mathematics classes. Both the Group Embedded Figures Test (GEFT) and the measure of Piagetian intellectual development stages were also significantly correlated with grade in the course. There was no relationship between grade and the personality type, as measured by the Myers-Briggs Type Indicator (MBTI); however, an interesting and distinctive personality profile was evident.

REFERENCES

• 1: Alspaugh, C. A. (1972). Identification of some components of computer programming aptitude. Journal of Research in Mathematics Education, 3, 89-98.
• 2: Richard H. Austing , Bruce H. Barnes , Della T. Bonnette , Gerald L. Engel , Gordon Stokes, Curriculum '78: recommendations for the undergraduate program in computer science— a report of the ACM curriculum committee on computer science, Communications of the ACM, v.22 n.3, p.147-166, March 1979 [doi>10.1145/359080.359083]
• 3: Ricky J. Barker , E. A. Unger, A predictor for success in an introductory programming class based upon abstract reasoning development, ACM SIGCSE Bulletin, v.15 n.1, p.154-158, February 1983
• 4: Bateman, C. R. (1973). Predicting performance in a basic computer course. Proceedings of the Fifth Annual Meeting of the American Institute for Decision Sciences, Boston, MA. 130-133.
• 5: D. F. Butcher , W. A. Muth, Predicting performance in an introductory computer science course, Communications of the ACM, v.28 n.3, p.263-268, March 1985 [doi>10.1145/3166.3167]
• 6: Patricia F. Campbell , George P. McCabe, Predicting the success of freshmen in a computer science major, Communications of the ACM, v.27 n.11, p.1108-1113, Nov. 1984 [doi>10.1145/1968.358288]
• 7: Louis W. Glorfeld , George C. Fowler, Validation of a model for predicting aptitude for introductory computing, ACM SIGCSE Bulletin, v.14 n.1, p.140-143, February 1982
• 8: Hassell, J. (1982). Cognitive style and a first course in computer science: A success story. AEDS Monitor, 21, 33-35.
• 9: Terry R. Hostetler, Predicting student success in an introductory programming course, ACM SIGCSE Bulletin, v.15 n.3, p.40-43, Sept. 1983 [doi>10.1145/382188.382571]
• 10: Keirsey, D., & Bates, M. (1984). Please understand me: Character & temperament types. DelMar: Prometheus Nemesis Book Company.
• 11: Kelly, E. J. (in press). Chessmaster personality and type: Comparative analyses with average players and non-players. Journal of Psychological Type.
• 12: Barry L. Kurtz, Investigating the relationship between the development of abstract reasoning and performance in an introductory programming class, ACM SIGCSE Bulletin, v.12 n.1, p.110-117, February 1980
• 13: John Konvalina , Stanley A. Wileman , Larry J. Stephens, Math proficiency: a key to success for computer science students, Communications of the ACM, v.26 n.5, p.377-382, May 1983 [doi>10.1145/69586.358140]
• 14: McCaulley, M. H., Godleski, E. S., Yokomoto, C. F., Harrisberger, L., & Sloan, E. D. (1983). Applications of Psychological Type in Engineering Education. Engineering Education, 73, 394-400.
• 15: Lawrence J. Mazlack, Identifying potential to acquire programming skill, Communications of the ACM, v.23 n.1, p.14-17, Jan. 1980 [doi>10.1145/358808.358811]
• 16: Myers, I. B. (1961). Manual for the Myers-Briggs Type Indicator. Palo Alto: Consulting Psychologists Press.
• 17: Myers, I. B. with P. B. Myers. (1980). Gifts Differing, Palo Alto: Consulting Psychologists Press.
• 18: Peter R. Newsted, Grade and ability predictions in an introductory programming course, ACM SIGCSE Bulletin, v.7 n.2, p.87-91, June 1975 [doi>10.1145/382205.382897]
• 19: Nowaczyk, R. H. (1983). Cognitive skills needed in computer programming. Paper presented at the Annual Meeting of the Southeastern Psychological Association. Atlanta, GA. (ERIC Document Reproduction Service No. ED 236 666).
• 20: Petersen, C. G., & Howe, T. G. (1979). Predicting academic success in introduction to computers. AEDS Journal, 12, 182-191.
• 21: Sorg, D. H., & Wark, L. K. (1984). Factors for success as a computer science major. AEDS Journal, 17, 36-45.
• 22: Stevens, D. J. (1983). Cognitive processes and success of students in instructional computer courses. AEDS Journal, 16, 228-233.
• 23: G. M. Weinberg, The psychology of computer programming, Van Nostrand Reinhold Co., New York, NY, 1988
• 24: Werth, L. H. (1985). Predicting Student Performance in a Beginning Computer Science Class. Unpublished dissertation. University of Nevada, Las Vegas. Las Vegas, NV.
• 25: Kenneth L. Whipkey, Identifying predictors of programming skill, ACM SIGCSE Bulletin, v.16 n.4, p.36-42, Dec. 1984 [doi>10.1145/382200.382544]
• 26: Wileman, S. A., Konvalina, J., & Stephens, L. J. (1981). Factors influencing success in beginning computer science courses. Journal of Educational Research, 74, 223-226.
• 27: Witkin, H. A. (1976). Cognitive style in academic performance and in teacher-student relations. In S. Messick (Ed.) Individuality in learning: Implications of cognitive style and creativity for human development. San Francisco: Jossey-Bass.
• 28: Witkin, H. A., Moore, C. A., Goodenough, D. R., & Cox, P. W. (1977). Field-dependent and field-independent cognitive styles and their educational implications. Review of Educational Research, 47, 1-64.
• 29: Witkin, H. A., Oltman, P. K., Raskin, E., & Karp, S. A. (1971). A manual for the embedded figures test. Palo Alto: Consulting Psychologists Press.

INDEX TERMS

Classification:
  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.2 Computer and Information Science Education
          Subjects: Computer science education

General Terms:
Human Factors, Measurement, Performance