Programming Skill Development in Computer Science Education: An Analysis of Gender, GPA, and Project Domain Influence
Abstract
This study examines the influence of gender, age, and academic performance (GPA) on proficiency in Python, SQL, and Java among computer science students, aiming to inform more inclusive and effective programming curricula. A quantitative approach was employed, with t-tests and ANOVA used to analyze differences in programming skills based on gender and age. Data were collected from undergraduate computer science students and evaluated using standardized programming assessments. The t-test results revealed a significant gender disparity in Python proficiency, with males scoring higher (M=2.8) than females (M=2.4; t=2.19, p=0.03). No significant gender differences were observed for SQL (t=-1.46, p=0.145) or Java (t=-1.21, p=0.227). ANOVA results indicated no significant differences in programming skills across age groups for Python (F=0.48, p=0.487), SQL (F=0.012, p=0.914), or Java (F=0.062, p=0.804). The findings suggest that gender influences Python proficiency, highlighting the need for targeted educational interventions to support female students. Conversely, instruction in SQL and Java may proceed without specific gender-based adjustments. This study underscores the importance of inclusive curricula and targeted support, such as mentorship programs and specialized training, to address gender disparities in programming education. By fostering equity, these efforts can better prepare students for success in the technology sector. Further research should explore additional factors influencing programming proficiency to refine educational strategies.
Keywords
Full Text:
PDFReferences
Ash, R. A., Dupont, B., Rosenbloom, J. L., & Coder, L. (2009). From the SelectedWorks of Joshua L. Rosenbloom Examining the Obstacles to Broadening Participation in Computing: Evidence from a Survey of Professional Workers Examining the Obstacles to Broadening Participation in Computing: Evidence from a Survey of Professional Workers. https://works.bepress.com/joshua_rosenbloom/29/
Baker, M. (2010). Choices or Constraints? Family Responsibilities, Gender and Academic Career. Journal of Comparative Family Studies, 41(1), 1–18. http://www.jstor.org/stable/41604335
Barroga, E., Matanguihan, G. J., Furuta, A., Arima, M., Tsuchiya, S., Kawahara, C., Takamiya, Y., & Izumi, M. (2023). Conducting and Writing Quantitative and Qualitative Research. J Korean Med Sci, 38(37). https://doi.org/10.3346/jkms.2023.38.e291
Beyer, S. (2023). Gender differences in computer science education: Addressing the gaps through inclusive learning strategies. Journal of Educational Technology and Innovation, 12(3), 145-162.
Borsotti, V. (2018). Barriers to gender diversity in software development education: actionable insights from a danish case study. Proceedings of the 40th International Conference on Software Engineering: Software Engineering Education and Training, 146–152. https://doi.org/10.1145/3183377.3183390
Buchholtz, N., & Vollstedt, M. (2024). Q methodology as an integrative approach: bridging quantitative and qualitative insights in a mixed methods study on mathematics teachers’ beliefs. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1418040
Budge, J., Charles, M., Feniger, Y., & Pinson, H. (2023). The gendering of tech selves: Aspirations for computing jobs among Jewish and Arab/Palestinian adolescents in Israel. Technology in Society, 73. https://doi.org/10.1016/j.techsoc.2023.102245
Charlesworth, T., & Banaji, M. R. (2022). Gender disparities in STEM education: Causes and interventions. Annual Review of Educational Psychology, 55(2), 89-112. https://doi.org/10.1146/annurev-educpsych-2022-110921
Dodgson, J. E. (2017). About Research: Qualitative Methodologies. Journal of Human Lactation, 33(2), 355–358. https://doi.org/10.1177/0890334417698693
Eberly, L. E., & Telke, S. E. (2011). Statistical Hypothesis Testing: Comparison of Means Across Multiple Patient Groups. Journal of Wound Ostomy & Continence Nursing, 38(2). https://journals.lww.com/jwocnonline/fulltext/2011/03000/statistical_hypothesis_testing__comparison_of.5.aspx
Febriansyar, R. A., Riyanto, T., Istadi, I., Anggoro, D. D., & Jongsomjit, B. (2023). Bifunctional CaCO3/HY Catalyst in the Simultaneous Cracking-Deoxygenation of Palm Oil to Diesel-Range Hydrocarbons. Indonesian Journal of Science and Technology, 8(2), 281–306. https://doi.org/10.17509/ijost.v8i2.55494
Guevara-Ramírez, P., Ruiz-Pozo, V. A., Cadena-Ullauri, S., Salazar-Navas, G., Bedón, A. A., V-Vázquez, J. F., & Zambrano, A. K. (2022). Ten simple rules for empowering women in STEM. PLOS Computational Biology, 18(12), e1010731-. https://doi.org/10.1371/journal.pcbi.1010731
Guzdial, M., & Morrison, B. (2016). Education: Growing computer science education into a STEM education discipline. In Communications of the ACM (Vol. 59, Issue 11, pp. 31–33). Association for Computing Machinery. https://doi.org/10.1145/3000612
Harred, R., Barnes, T., Fisk, S. R., Akram, B., Price, T. W., & Yoder, S. (2023). Do Intentions to Persist Predict Short-Term Computing Course Enrollments? A Scale Development, Validation, and Reliability Analysis. SIGCSE 2023 - Proceedings of the 54th ACM Technical Symposium on Computer Science Education, 1, 1062–1068. https://doi.org/10.1145/3545945.3569875
Hinckle, M., Rachmatullah, A., Mott, B., Boyer, K. E., Lester, J., & Wiebe, E. (2020a). The Relationship of Gender, Experiential, and Psychological Factors to Achievement in Computer Science. Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education, 225–231. https://doi.org/10.1145/3341525.3387403
Hinckle, M., Rachmatullah, A., Mott, B., Boyer, K. E., Lester, J., & Wiebe, E. (2020b). The Relationship of Gender, Experiential, and Psychological Factors to Achievement in Computer Science. Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education, 225–231. https://doi.org/10.1145/3341525.3387403
Hostetler, T. R. (1983). Predicting student success in an introductory programming course. SIGCSE Bull., 15(3), 40–43. https://doi.org/10.1145/382188.382571
Jabbar, R. A., Dayana, N., & Halim, A. (2024). The Effects of Technology-Integrated Project-Based Learning on Students’ Acquisition of Programming Abilities in Computer Science Courses. In J. Electrical Systems (Vol. 20, Issue 4).
Jaccheri, L. (2022). Gender Issues in Computer Science Research, Education, and Society. Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1, 4. https://doi.org/10.1145/3502718.3534204
Kallio, R. E. (1995). Factors influencing the college choice decisions of graduate students. Research in Higher Education, 36(1), 109-124. https://doi.org/10.1007/BF02207769
Li, Y., Zhang, X., & Chen, L. (2022). Academic performance and technical skill acquisition: A longitudinal study. Educational Research and Reviews, 45(2), 101-118.
Li, T., Wang, W., Li, Z., Wang, H., & Liu, X. (2022). Problem-based or lecture-based learning, old topic in the new field: a meta-analysis on the effects of PBL teaching method in Chinese standardized residency training. BMC Medical Education, 22(1). https://doi.org/10.1186/s12909-022-03254-5
López-Pimentel, J. C., Medina-Santiago, A., Alcaraz-Rivera, M., & Del-Valle-soto, C. (2021). Sustainable project-based learning methodology adaptable to technological advances for web programming. Sustainability (Switzerland), 13(15). https://doi.org/10.3390/su13158482
Lorås, M., Sindre, G., Trætteberg, H., & Aalberg, T. (2022). Study Behavior in Computing Education—A Systematic Literature Review. ACM Transactions on Computing Education, 22(1), 1–40. https://doi.org/10.1145/3469129
Milutinović, V. (n.d.). Unlocking the Code: Exploring Predictors of Future Interest in Learning Computer Programming Among Primary School Boys and Girls. International Journal of Human–Computer Interaction, 1–18. https://doi.org/10.1080/10447318.2024.2331877
Moya, J., Flatland, R., Matthews, J. R., White, P., Hansen, S. R., & Egan, M. L. (2023). “i Can Do That Too”: Factors Influencing a Sense of Belonging for Females in Computer Science Classrooms. SIGCSE 2023 - Proceedings of the 54th ACM Technical Symposium on Computer Science Education, 1, 680–686. https://doi.org/10.1145/3545945.3569860
Murphy, L., Richards, B., McCauley, R., Morrison, B. B., Westbrook, S., & Fossum, T. (2006). Women catch up: gender differences in learning programming concepts. SIGCSE Bull., 38(1), 17–21. https://doi.org/10.1145/1124706.1121350
Newsted, P. R. (1975). Grade and ability predictions in an introductory programming course. SIGCSE Bull., 7(2), 87–91. https://doi.org/10.1145/382205.382897
Niousha, R., Saito, D., Washizaki, H., & Fukazawa, Y. (2023). Gender Characteristics and Computational Thinking in Scratch. Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2, 1344. https://doi.org/10.1145/3545947.3576290
Omeh, C. B., Olelewe, C. J., & Nwangwu, E. C. (2024). Fostering computer programming and digital skills development: An experimental approach. Computer Applications in Engineering Education, 32(2), e22711. https://doi.org/https://doi.org/10.1002/cae.22711
Palid, O., Cashdollar, S., Deangelo, S., Chu, C., & Bates, M. (2023). Inclusion in practice: a systematic review of diversity-focused STEM programming in the United States. In International Journal of STEM Education (Vol. 10, Issue 1). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1186/s40594-022-00387-3
Ranjeeth, L., & Padayachee, I. (2024). Factors that influence computer programming proficiency in higher education: A case study of Information Technology students. South African Computer Journal, 36(1), 40–75. https://doi.org/10.18489/SACJ.V36I1.18819
Schindler, C., & Müller, M. (2019). Gender gap? a snapshot of a bachelor computer science course at Graz University of Technology. Proceedings of the 13th European Conference on Software Architecture - Volume 2, 100–104. https://doi.org/10.1145/3344948.3344969
Stringer, L. R., Lee, K. M., Sturm, S., & Giacaman, N. (2024). The impact of professional learning and development on primary and intermediate teachers’ digital technologies knowledge and efficacy beliefs. Australian Educational Researcher. https://doi.org/10.1007/s13384-024-00716-1
Tuson, E. (2023). Applications of Programming as Theory Building in Computer Science Education. Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2, 621–622. https://doi.org/10.1145/3587103.3594137
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4. https://doi.org/10.1016/j.caeai.2023.100147
Yuan, H., Yuan, W., Duan, S., Yong, R., Jiao, K., Wei, Y., Leach, M., Li, N., Zhang, X., Lim, E. G., & Song, P. (2024). Navigating the uncertainty: the impact of a student-centered final year project allocation mechanism on student performance. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-03324-7
DOI: https://doi.org/10.35445/alishlah.v16i4.5943
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Yogi Yunefri
Al-Ishlah Jurnal Pendidikan Abstracted/Indexed by:
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.