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

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Contributing to Success in Computer Science A
Study of Twelve Factors

• Sharon A. Shrock, Ph.D.
• Curriculum Instruction Department
• Southern Illinois University at Carbondale

• Brenda Cantwell Wilson, Ph.D.
• Computer Science Department
• Murray State University

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What Are the Factors for Success in Computer
Science?
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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

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1. Previous Programming Experience

• Formal programming class
• Self-initiated programming

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2. Previous Non-Programming Experience

• Internet searches, e-mail, chat rooms, discussion
  groups
• Playing Games
• Productivity Software

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3. Attribution for Success/Failure

• Ability (stable)
• Difficulty of task (stable)
• Luck (unstable)
• Effort (unstable)

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4. Self-efficacy

• Feeling about ones ability to perform C
  programming tasks
• Measured by Ramalingam Wiedenbecks Computer
  Programming Self-efficacy Scale

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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

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6. Encouragement from Others

• Words of confidence, praise, or discussions to
  encourage student to study computer science

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7. Work Style Preference

• Individual / competitive work
• Group/cooperative work

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8. Math Background

• Number of semesters of math taken in high school.

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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 gt .70)
• Math background was included as predictor variable

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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)

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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?

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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?

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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?

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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

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Methodology Instruments

• Questionnaire
• The Computer Programming Self-Efficacy Scale

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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

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The Computer Programming Self-Efficacy Scale

• Developed by Ramalingam Weidenbeck (1998)
• Based on Banduras 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)

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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)

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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

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Methodology Analysis of Data

• Alpha level .05
• Multiple Regression Study
• Correlation matrix for all predictor variables
  and criterion variable

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Methodology Analysis of Data Residual Plot
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Methodology Analysis of Data
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Results Research Question 1- Proportion of
Variance

• Prediction of midterm with full model
• R2 .4443, F(12,92) 6.13, p
  .0001

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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

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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)

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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)

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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

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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.

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Contributing to Success in Computer Science A
Study of Twelve Factors

• E-mail
• Brenda.Wilson_at_murraystate.edu
• Website
• http//campus.murraystate.edu/academic/faculty/br
  enda.wilson/homepage.html
• Click on SIGCSE link