Contributing to Success in Computer Science: A Study of Twelve Factors

1. Brenda Cantwell Wilson, Ph.D. Computer Science Department Murray State University Sharon A. Shrock, Ph.D. Curriculum & Instruction Department Southern Illinois University at Carbondale Contributing to Success in Computer Science: A Study of Twelve Factors

2. What Are the Factors for Success in Computer Science?

3. Possible Factors for Success… • Previous programming experience • Previous non-programming computing experience • Attribution for success/failure (4 possibilities) • Self-efficacy • Comfort level • Encouragement from others • Work style preference • Math background • ACT math score *** (had to delete) • Gender

4. 1. Previous Programming Experience • Formal programming class • Self-initiated programming

5. 2. Previous Non-Programming Experience • Internet searches, e-mail, chat rooms, discussion groups • Playing Games • Productivity Software

6. 3. Attribution for Success/Failure • Ability (stable) • Difficulty of task (stable) • Luck (unstable) • Effort (unstable)

7. 4. Self-efficacy • Feeling about one’s ability to perform C++ programming tasks • Measured by Ramalingam & Wiedenbeck’s Computer Programming Self-efficacy Scale

8. 5. Comfort Level • Asking/answering questions in class • Asking questions in lab • Getting help during office hours • Perceived anxiety – assignments • Perceived difficulty of course • Perceived difficulty of writing programs • Perceived understanding in class compared to classmates

9. 6. Encouragement from Others Words of confidence, praise, or discussions to encourage student to study computer science

10. 7. Work Style Preference • Individual / competitive work • Group/cooperative work

11. 8. Math Background Number of semesters of math taken in high school.

12. 9. ACT math score -- deleted • 45% of 105 subjects did not have ACT scores recorded at SIUC • ACT math score did not show significant difference in multiple regression test for students with these scores (p > .70) • Math background was included as predictor variable

13. Measure of Success in Computer Science Course • Midterm grade (percent score 0-100) • High attrition rate in 1st computer science course • Consulted with several seasoned computer science professors, including the instructor of this course • Correlation coefficient (Midterm vs. Final Grade) = .97173, p = .0001, N = 48 (calculated for two sections of 1st CS course – Fall 1999)

14. Research Question #1: What Is the Proportion of Variance in Midterm Grade Accounted for by the Linear Combination of the Factors: Previous Programming Experience, Previous Non-programming Experience, Attribution for Success/failure, Self-efficacy, Comfort Level, Encouragement From Others, Work Style Preference, Math Background, and Gender?

15. Research Question #2: What is the contribution of each factor over and above the contribution of the other factors in the prediction of the midterm course grade?

16. Research Question #3: Are certain types of previous computing experiences (a programming class; self-initiated programming; use of internet, e-mail, chat rooms, and/or discussion groups; playing games on the computer; use of productivity software) predictive of success in a college introductory computer science class?

17. Methodology: Subjects • CS 1 • 1st programming class required for CS major • 130 students enrolled for spring 2000 • Voluntary participation • 105 subjects were used • 19 females (18%) • 29% freshmen, 29% sophomores, 22% juniors, 12 % seniors, 8% graduate students

18. Methodology: Instruments • Questionnaire • The Computer Programming Self-Efficacy Scale

19. Questionnaire • Pilot tested using MSU CS 1 students • Evaluation of instrument for validity • Face validity evaluated by 3 test & evaluation experts • Content validity evaluated by 4 computer science experts • Evaluation of instrument for reliability • Math background, r = .98 • Previous programming, r = 1.0 • Previous self-initiated programming, r = .72 • Previous non-programming, r = .95 • Work style preference, r = .80 • Comfort level, r = .88 • Attitude toward exam grade, r = .77 • Attributions, r = .72 • Encouragement, r = 1.0

20. The Computer Programming Self-Efficacy Scale • Developed by Ramalingam & Weidenbeck (1998) • Based on Bandura’s argument • Must be measured in specific domain of activity • Includes • Magnitude (level of task difficulty) • Strength (certainty of efficacy judgment) • Generality (extent that belief holds across different situations)

21. The Computer Programming Self-Efficacy Scale • 32 items specific to C++ programming • Questions about designing, writing, comprehending, modifying, and reusing programs • 7-point Likert-type scale (1- not confident at all to 7 – absolutely confident) • Overall alpha reliability coefficient = .98 & .97 for two administrations of test (tested on 421 students)

22. Methodology: Procedure • Human Subjects Committee approval • Data collected after the first major exam & before midterm (questionnaire, CPSE Scale, consent form for obtaining ACT & midterm scores) • ACT math score predictor variable dropped

23. Methodology: Analysis of Data • Alpha level = .05 • Multiple Regression Study • Correlation matrix for all predictor variables and criterion variable

24. Methodology: Analysis of Data – Residual Plot

25. Methodology: Analysis of Data

26. Results: Research Question #1- Proportion of Variance • Prediction of midterm with full model • R2 = .4443, F(12,92) = 6.13, p = .0001

27. Results: Research Question #2- Predictive Factors • GLM evaluating Type I & Type III sums of squares • 3 significant predictors • Comfort Level (p = .0002) positive influence • Math Background (p = .0050) positive influence • Attribution to Luck (p = .0233) negative influence

28. Results: Research Question #2 - adhoc • Stepwise Multiple Regression • 5 factor model • Comfort Level (positive correlation) • Math Background (positive correlation) • Attribution to Luck (negative correlation) • Work Style Preference (indiv/competitive) • Attribution to Task Difficulty (negative correlation)

29. Results: Research Question #3 – Previous Computing • Multiple Regression on 5 types of previous computing experiences & midterm grade (R2 = .15, p = .0041) • 2 significant predictors • Previous Programming Course(positive correlation) • Playing Games(negative correlation)

30. Recommendations: For Practice • Provide environment which encourages students to ask/answer questions in/out of class, free of intimidation • Provide opportunities for students to get help • Smaller numbers in classes • Stress math background in advising students • Match between class assignments & test questions to eliminate attribution to luck

31. Recommendations: For Further Research • Further study on comfort level • Replication of this study with top five predictor variables • Study why female students choose CS • Qualitative study • Influences from childhood • Personality traits such as confidence, perseverance, work style preference.

32. E-mail: Brenda.Wilson@murraystate.edu Website: http://campus.murraystate.edu/academic/faculty/brenda.wilson/homepage.html Click on SIGCSE link Contributing to Success in Computer Science: A Study of Twelve Factors