Computer programming skill and gender difference: An empirical study

Transcription

1 AMERICAN JOURNAL OF SCIENTIFIC AND INDUSTRIAL RESEARCH 2015,Science Huβ, ISSN: X, doi: /ajsir Computer programming skill and gender difference: An empirical study Solomon Olalekan AKINOLA Department of Computer Science, University of Ibadan, Nigeria ABSTRACT Women have been said to be poorly represented in computing most especially in computer programming. However, there is a dearth of empirical research to support this evidence. This study employed an experimental proof approach to verify the wide acclaimed gender difference in computing. Two parallel student-gender groups (male and Female) were subjected to two different computer programming problems. The problems were given to them to solve, starting from the analysis phase to the final implementation. Their outcomes measured in terms of the accuracy and efficiency of codes turned out from the experiments were then compared statistically. Briefly, the results showed that gender difference may not come into play at all when it comes to computer programming. Other factors like fear, interest or attitudes may however be responsible for the low representation of females in computing. Keywords: Gender difference, Computer programming skill, Empirical study, Collaborative Programming, Pair programming. INTRODUCTION Computer Programming is known to be a complex task that is difficulty to do. Consequently, learning computer programming is one of the first and most challenging tasks encountered by computing students. The difficulties faced by the students can be perceived by the high degrees of failure and the difficulties presented in the courses directly dependent on the abilities to program, to develop a logical reasoning and to solve problems. In part, this is the result of the difficulties found by instructors to effectively guide the students during their programming lab activities, due to the large number of students per class (Eustáquio et al., 2006). In the work of Dawn (2005), women have been highly influential in the field of computer programming since its supposed inception in the 1830s. Ada Byron, Countess of Lovelace and famed collaborator of Charles Babbage, is often credited as being the world's first computer programmer. Perhaps even more influential to the field was naval officer Grace Murray Hopper contributed to the development of COBOL (Common Business Oriented Language), the first business-oriented programming language, released in 1960 and still in use today. These two women are the most notable from a tradition of female computer programmers that persisted throughout 1960s. For example in 1960, 65% of the United State citizens' estimated 2000 computer programmers were women (Lockheed, 1993). Interestingly, the percentage of female computer programmers began to decline sharply in the 1980s with advent of the personal computer or more specifically, when computer began to appear in homes. Even as job opportunities for programmers and software developers peaked in the 1990s, the number of women pursing such opportunities continued to decline. According to U. S. Department of Labor, Bureau of Labor Statistics as at 2012, computer programming is very much a male dominated field, with women making up only 22.5% of computer programmers nationwide, with not even half that percentage of women represented in software development and engineering jobs. The big question is Does gender difference really affects the programming skills of the professionals? Previous studies in this area of research have used observation, interview or questionnaire instruments to study the gender difference in computing. However, this study was designed to empirically proof that no gender is better than the other in programming task using experimental proof approach. The rest of this paper is organized as follows. Related works are presented in section 2 while the research methodology is presented in Section 3. In 1

2 section 4, we present and discuss the results obtained in the study. Section 5 concludes the paper. Related Works: Women are severely underrepresented in the field of Computer Science. Attitude of women toward computing is one of several factors that might explain the low participation of women in computing. Programming is not alluring to females. This could be attributed to females' low confidence and programming abilities. Therefore, their attitudes towards programming are more negative than males as found by Korkmaz and Altun (2013). Gender influences how people will perform within computing contests, although it is presumable that there are numerous other variables, such as ethnicity, personality, experience, and so on. In addition, casual observations, as well as various reports (for example, Helgeson, 2005), reveal a systemic underrepresentation of women in Computer Science (Maryanne and Anthony, 2006). Severiens and Dam (1994) performed a metaanalysis of the gender effect on two learning style instruments, namely Kolb s Learning Style Inventory and Entwistle s Approaches to Studying Inventory, and found a significant gender preference on subscales of these instruments. Men tended to prefer the abstract conceptualization mode of learning more than women, according to Kolb s instrument. Men were likely to adopt a deep approach, be extrinsically motivated and achievement oriented. Although females are able and willing to compete, and do so well in specific situations, the widely held notion that they tend to be more cooperative and less competitive than males does hold merit. In general, findings from social and developmental psychology indicate that females prefer cooperative activities, while boys prefer competitive activities. A study by West (2001) show that there was no difference in learning styles between female majors, female nonmajors, and male majors in computing. According to Cashdan (1999) as cited by Maryanne and Anthony, (2006), women often feel pressured by popular stereotyping to be considered as passive and feminine, and thus actively hide their competitive nature. In general, women tend to choose high probability, low payoff strategies, whereas men tend to choose, under the same situations, low probably but high payoff strategies (Daly and Wilson, 2001). This difference in strategy may lead to differences in programming contest performance. It is predicted that men will be more likely to rush at forming a solution, completing it as quickly as possible, and then submit it for judging. Women will be more likely to devote more time to conceptualizing a solution, spend longer working on an individual solution, and then submit it for judging. This difference means that women will have less opportunity to try various solutions, in the belief that they have increased their accuracy by spending longer on a solution (Maryanne and Anthony, 2006). Thus, within the context of computer programming contests, women may be perceived as less competent because they adopt less risky approaches. This matter is even more deleterious, given that stereotypical beliefs, such as people view me as less competent because I am female often lead directly to depressed performance (Steele and Aronson, 1995). Second, rather than gambling on their ability to guess the correct approach, women will take more time to explore one solution as opposed to implementing a fast and dirty solution that may not work. This less risky strategy will allow less time for trying a wider assortment of potential solutions, and less time to attempt remaining questions. Third, women will not guess at a response as readily as men, nor direct team mates to consider a possible solution, when they are not sure of themselves. They will also feel uncomfortable when, in a mixed-sex team, they are asked to pursue high-risk strategies (Maryanne and Anthony, 2006). Anxiety can play important role in gender computer programming. Women experience a decrease in selfconfidence when they perceive that their work will be compared to others for evaluation (Lenney, 1997). When there is no pressure (i.e., in a non-competitive environment), there is equivalent enthusiasm reported between women and men for computing activities (Astrachan, 2004). Self-efficacy has been described as a critical element in accomplishing tasks successfully. In developing and validating a computer user self-efficacy (CUSE) scale, Cassidy and Eachus (2002) found that males had consistently higher self-efficacy than females irrespective of whether or not a computer training course had been attended before. Perceived masculinity of the task did not seem to explain the gender differences in computer self-efficacy. Experience with computers and familiarity with computer software packages were the two most important predictors of computer self-efficacy. In a literature review of research on the gender digital divide, Cooper (2006) argued that the digital divide can be boiled down to a problem of computer anxiety. 2

3 She further proposed a causal model incorporating gender stereotype, attribution patterns, and stereotype threat as the predecessors of computer anxiety, and computer anxiety as a predecessor of computer attitude and computer performance. Noreen (1985) investigated gender differences in planning and debugging behavior, group processes, and achievement among junior high school students learning computer programming in small group or individual settings. Results from the study shows that in neither study and in neither setting, group or individual, did females and males differ in behavior or learning outcomes. Furthermore, the composition of the group, the ratio of females to males, had little effect on behavior or achievement. Pioro (2006) in his work also found out that In general, the gender and academic major factors were not found to be significant in computer programming. Stoilescu and Egodawatte (2010) in their work on Gender differences in the use of computers, programming, and peer interactions in computer science classrooms show that in computer programming, female students were not so involved in computing activities whereas male students were heavily involved. Research Methodology In this work, we try to find out empirically if gender difference will really affect the programming skill acquisition by students using a collaborative learning approach as suggested by Mustafa (2013). He asserts that collaborative programming could be a chance to dissipate gender differences in attitudes towards programming. Subjects: The subjects used for this research were the volunteered Students in 200 level of the Department of Computer Science, University of Ibadan, Ibadan, Nigeria. It is appropriate to use students of Computer Science at 200 Level who are just learning how to program because the effect of collaboration can easily be noticed without wasting much resource. The Students got little or no prior knowledge of Java programming. They were taken through a 13-week teaching and practical classes in Object Oriented Programming (OOP) concepts in CSC 232 (Structured Programming) Courses in the first semester of 2014/2015 Session. Experimental Design: Two experiments were organized for the students. The first experiment was carried out by 68 students in pair programming mode, with 22 male and 12 female pair groups. The second experiment involved 72 students in four collaborative team sizes, with 12 male and 6 female groups. Two different programming exercises were given the students to tackle during the experiments. Variables: Two major independent variables were manipulated in the study: Gender and Team sizes. The only dependent variable measured was the outcomes of the individual groups in the experiments. The groups were scored based on the efficiency and accuracy of the turned-out codes for 10 marks in each of the experiments. Efficiency was determined via the total lines of code produced, use of proper programming logic and control structures and use of Object Oriented concepts. The accuracy was determined via adherence to problem specification by the groups. Experimental Set Up and Implementation: The artifacts used for this experiment were two practical programming questions (See the Appendix) that involved the use of Object Oriented Programming (OOP) concepts, data structures as well as control structures. The teams individually worked collaboratively to solve the problems right from the analysis phase to implementation, using a single computer system allotted to them. The first programming exercise involved a payroll program using files. The volunteered students were distributed into pair groups of same gender. Twentytwo male and twelve female pair programmers participated in the exercise. The pair groups worked independently on the problem with one member of each group serving as the Driver and another serving as the Navigator. Each pair group analyzed the problem, designed the algorithm for the solution to the problem at hand, implemented the algorithm with Java programming language and debugged the code together. The second programming task involved implementing a solution for a supermarket with Java Object Oriented concepts and Database Connectivity with Java. The students were randomly organized into collaborative teams of size four of the same gender. Twelve male and six female independent collaborative teams of size four emerged. Again, each group analyzed the problem, designed the algorithm for the solution to the problem at hand, implemented the algorithm with Java programming language and debugged the code together. During the practical session, all the four members discussed 3

4 the problem together with one of them serving as the Driver. Each parallel teams was served with the practical questions and a Report Form that was filled during each stage of the programming task. The teams also submitted their final codes as well as sample output screen shots if they were successful. The team identification number was filled on the Reporting form in order to identify each team. The two experiments (programming tasks) were run for a maximum of three hours each in the Computer Laboratory of the Department of Computer Science, University of Ibadan, Nigeria for two days; one per week. The researcher served as the coordinator for the period of the experiments. Most importantly, the researcher ensured that there was no interaction between different groups during the practical sessions such that ideas were not shared among the independent groups. Scoring and analysis of the codes were carried out using descriptive, t-test and ANOVA statistics. RESULTS AND DISCUSSION Main Results: Table 1 shows the results of the outcomes (Mean Scores) of the groups obtained from the two experiments involving independent gender groups. Figure 1 shows the chart for the outcomes. Table 1: Mean Outcomes of the Independent Groups in the Two Experiments Team Male 2-Person Team Female 2-Person Team Male 4-Person Team Female 4-Person Team Mean Outcomes SEM SEM SEM SEM Note: SEM means Standard Error of the Mean Fig. 1: Chart of the Mean Outcomes of the Independent Gender Groups From Table 1 and Figure 1, it could be inferred that the female pair group performed marginally better than the male counterpart. However, the 4-person male teams performed better than their female counterpart in the 4-person collaborative teams (second experiment). Further Statistical Analysis: The mean outcomes of both males and females teams were tested using t- test. Table 2 gives the result of the analysis. 4

5 Table 2: Independent Sample t-tests Data Hypothesis Tested p-value Mean Values Decision Mean Programming Outcomes of gender pair groups Mean Programming Outcomes of gender 4- Person groups Ho: There is no significant difference in the programming outcomes of both genders in pair programming 0.66 Male = 7.0 Ho: There is no significant difference in the programming outcomes of both genders in 4- person collaborative programming teams Female = Male = 6.7 Female = 6.2 Ho accepted p>0.05 Ho accepted p>0.05 Table 2 shows that there is no significant difference between male and female programming teams outcomes (p>0.05) in both experiments. A One-Way Analysis of Variance (ANOVA) was carried out on all the results obtained from the two experiments. Results of the analysis show that there were no significant differences among the treatment groups (Males and females Pair versus 4-person collaborative teams, p = 0.15). DISCUSSION OF RESULTS Observations in recent years show that women participation in the area of serious programming in the software industry is very low (Korkmaz and Altun, 2013 and U. S. Department of Labor, Bureau of Labor Statistics, 2012). This study was motivated by this observation to probe empirically on whether gender difference exists or not among student programmers at the University of Ibadan, Nigeria. The hypotheses tested in this study reiterate the fact that gender difference may not come into play in computing. The means of their average effectiveness were very close to each other. The mean outcome is a totality of their performances in the analysis of the problem given, their accuracy and efficiency scores in the programming task. This buttresses the fact that the effectiveness of both gender teams was not different. This result confirms the qualitative studies earlier conducted by Noreen (1985), West (2001), Astrachan, (2004) and Pioro (2006) who showed that both genders can perform at par in computing. In fairness to both genders, their mean outcomes were marginally different and their difference quite insignificant at the end of the experiment. Both the genders performed more or less at par in the two experiments conducted. This boils down also to say that gender is not an issue at all in Computer programming. The results suggest that some other factors like interest or fear may be responsible for low representation of women in computing which is experienced today in students adopting programming as laudable computing profession. Attitude of women toward computing is one among several factors that might explain the low participation of women in computing. Programming is not alluring to females at all especially in the developing parts of the world such as Nigeria. This could be attributed to females' low confidence in Computer Programming. Therefore, their attitudes towards programming are more negative than males as found by Korkmaz and Altun (2013). Limitation of these Results / External Threat to Validity: The results obtained from this study are limited to academic environment and therefore, not generalizable for now. The experiment may need to be redone at industrial environments in order to verify the claims made in this study. Conclusion: In this study, we have empirically demonstrated that gender difference may not come into play at all when it comes to computer programming. Other factors like fear or attitudes may however be responsible for the low representation of females in computing. In building the females confidence in computing, collaborative programming approach as was used in this study is highly 5

6 encouraged and recommended to the tutors in teaching programming to students, as it could be a chance to dissipate gender differences in attitudes towards programming (Mustafa, 2013). ACKNOWLEDGEMENT: The researcher specially acknowledges the 200 Level students of the Department of Computer Science, University of Ibadan, Nigeria for their sincere and faithful participation in this study. The study was carried out as part of their regular teaching and practical periods in the First Semester of 2014/2015 session. REFERENCES Astrachan O. (2004). Non-competitive programming contest problems as a basis for just-in-time teaching. In: Proc. ASEE/IEEE Frontiers in Education Conference.Savannah, Georgia, pp. T3H/20 T3H/24. Cashdan, E. (1999). Are men more competitive than women? British Journal of Social Psychology, 37, Cassidy, S. & Eachus, P. (2002). Developing the computer user self-efficacy (CUSE) scale: Investigating the relationship between computer self-efficacy, gender and experience with computers. Journal of Educational Computing Research, 26(2), Cooper, J. (2006). The digital divide: The special case of gender. Journal of Computer Assisted Learning, 22(5), Daly, M., and M. Wilson (2001). Risk-taking, intrasexual competition, and homicide. Nebraska Symposium on Motivation, 47, Dawn Patitucci (2005). Gender and Programming Language Preferences of Computer Programming students at Moraine Valley Community College. In Master Degree (Project) submitted to Department of Occupational and Technical Studies Old Dominium University.digital.lib.odu.edu:8000/dspace/bitstream/.../ 326/1/patitucci_dawn.pdf Downloaded 20th July, Eustáquio São José de Faria, Juan Manuel Adán-Coello, Keiji Yamanaka (2006). Forming Groups for Collaborative Learning in Introductory Computer Programming Courses Based on Students Programming Styles: An Empirical Study. 36th ASEE/IEEE Frontiers in Education Conference. ers/1170.pdf Downloaded on 23rd May, Helgeson, V. (2005). Psychology of Gender (2nd edition). Prentice-Hall, Upper Saddle River, New Jersey. Korkmaz Ö. and Altun H.(2013). Engineering and CEIT student s attitude towards learning computer programming. International Journal of Social Science, 6(2): Lenney E. (1977). Women s self-confidence in achievement setting. Psychological Bulletin, 84, Lockheed Marlaine E.(1993). Women, Girls, and Computers: A First Look at the Evidence Sex Roles 29, Maryanne FISHER & Anthony COX(2006) Gender and Programming Contests: Mitigating Exclusionary Practices (Internet) Downloaded on 15th June, Mustafa Baer (2013). Attitude, Gender and Achievement in Computer ProgrammingMiddle-East Journal of Scientific Research 14 (2): , 2013 ISSN Noreen M. Webb (1985). The Role of Gender in Computer Programming Learning Processes, Journal of Educational Computing Research, Volume 1, No. 4, pp Pioro B. T. (2006). Introductory computer programming: gender, major, discrete mathematics, and calculus, Journal of Computing Sciences in Colleges, Volume 21 Issue 5, May 2006, USA, pp Severiens, S. E. & Ten Dam, G. T. M. (1994). Gender differences in learning styles: A narrative review and quantitative meta-analysis. Higher Education, 27(4), Steele, C., and J. Aronson (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69, Stoilescu, Dorian; Egodawatte, Gunawardena (2010). Gender differences in the use of computers, programming, and peer interactions in computer science classrooms, Computer Science Education, Volume 20, Number 4, pp (18) U.S. Department of Labor, Bureau of Labor Statistics (2004). Women in the Labor Force: A Databook (Report 973), Downloaded 20th August, 2012 West, Margaret Lynne Perkins (2001), "Gender underrepresentation in beginning computer programming courses", PhD Dissertations. Paper downloaded in May