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Understanding student retention in computer science education: The role of environment, gains, barriers and usefulness

Education and Information Technologies volume 22, pages 2365–2382 (2017)Cite this article

Abstract

Researchers have been working to understand the high dropout rates in computer science (CS) education. Despite the great demand for CS professionals, little is known about what influences individuals to complete their CS studies. We identify gains of studying CS, the (learning) environment, degree’s usefulness, and barriers as important predictors of students’ intention to complete their studies in CS (retention). The framework aims to identify reasons that may contribute to dropout, using responses from 344 CS students. The eight-predictor model accounts for 39 % of the explained variance in student retention. A high level for degree’s usefulness has a positive effect on retention. Further, cognitive gains and supportive environment positively impact degree’s usefulness, while non-cognitive gains hinder it. Lastly, negative feelings (personal values) are found to reduce student retention. The overall outcomes are expected to contribute to theoretical development, in order to allow educators and policy makers to take appropriate measures to enhance students’ experience in CS studies and increase retention.

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References

  • ACM/IEEE Computer Engineering Curricula, (2016). Curriculum Guidelines for Undergraduate Degree Programs in Computer Engineering. Technical report, Association for Computing Machinery (ACM) IEEE Computer Society.

  • ACM/IEEE Computing Curricula Task Force (2013). Computer science curricula 2013: Curriculum guidelines for undergraduate degree programs in computer science. Technical report, Association for Computing Machinery (ACM) IEEE Computer Society.

  • Araque, F., Roldan, C., & Salguero, A. (2009). Factors influencing university dropout rates. Computers and Education, 53(3), 563–574.

    Article  Google Scholar 

  • Barker, L. J., McDowell, C., & Kalahar, K. (2009). Exploring factors that influence computer science introductory course students to persist in the major. SIGCSE Bull, 41(1), 153–157.

    Article  Google Scholar 

  • Barker, L., Hovey, C. L., & Thompson, L. D. (2014). Results of a large-scale, multi-institutional study of undergraduate retention in computing. In Frontiers in Education Conference (FIE) (pp. 1–8). IEEE.

  • Bernold, L., Spurlin, J., & Anson, C. (2007). Understanding our students: A longitudinal study of success and failure in engineering with implications for increased retention. Journal of Engineering Education, 96(3), 263–274.

    Article  Google Scholar 

  • Biggers, M., Brauer, A., & Yilmaz, T. (2008). Student perceptions of computer science: A retention study comparing graduating seniors with CS leavers. ACM SIGCSE Bulletin, 40(1), 402–406.

    Article  Google Scholar 

  • Blickenstaff, J. C. (2005). Women and science careers: leaky pipeline or gender filter? Gender and Education, 17(4), 369–386.

    Article  Google Scholar 

  • Byrne, B. (2009). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming. Taylor & Francis.

  • Carter, L. (2006). Why students with an apparent aptitude in computer science don’t choose to major in computer science. In Proceedings of the Technical Symposium on. Computer Science Education, 27–31.

  • Chow, A., Eccles, J. S., & Salmela-Aro, K. (2012). Task value profiles across subjects and aspirations to physical and IT-related sciences in the United States and Finland. Developmental Psychology, 48(6), 1612–1628.

    Article  Google Scholar 

  • Cohoon, J. M. (2006). Just get over it or just get on with it. Retaining women in undergraduate computing. In J. Cohoon & W. Aspray (Eds.), Women and information technology: Research on underrepresentation, 205–238.

  • Duque, L. C. (2014). A framework for analysing higher education performance: Students’ satisfaction, perceived learning outcomes, and dropout intentions. Total Quality Management & Business Excellence, 25(1–2), 1–21.

    Article  Google Scholar 

  • European Commission (2015). Skills & Jobs. Retrieved 23 Dec. 2015 from: http://ec.europa.eu/digital-agenda/en/skills-jobs

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Hein, G. L., Bunker, K. J., Onder, N., Rebb, R. R., Brown, L. E., & Bohmann, L. J. (2012). University studies of student persistence in engineering and computer science. In American Society for Engineering Education.

  • Huang, P., & Brainard, S. G. (2001). Identifying determinants of academic self-confidence among science, math, engineering and technology students. Journal of Women and Minorities in Science and Engineering, 7(4), 315–337.

    Article  Google Scholar 

  • ICT Skills Action Plan. (2014–2018). Government, education and industry working together to make Ireland a global leader in ICT talent. Retrieved 19 Feb. 2016 from: http://cork.etb.ie/wp-content/uploads/sites/20/2014/08/14042014-ICT_Skills_Action_Plan-Publication.pdf

  • Jacobs, J. E. (2005). Twenty-five years of research on gender and ethnic differences in math and science career choices: What have we learned? New Directions for Child and Adolescent Development, 110, 85–94.

    Article  Google Scholar 

  • Johnson, A. C. (2007). Unintended consequences: how science professors discourage women of color. Science Education, 91(5), 805–821.

    Article  Google Scholar 

  • Johnson, D. R., Wasserman, T. H., Yildirim, N., & Yonai, B. A. (2014). Examining the effects of stress and campus climate on the persistence of students of color and White students: An application of Bean and Eaton’s psychological model of retention. Research in Higher Education, 55(1), 75–100.

    Article  Google Scholar 

  • Joo, Y. J., Lim, K. Y., & Kim, E. K. (2011). Online university students' satisfaction and persistence: Examining perceived level of presence, usefulness and ease of use as predictors in a structural model. Computers & Education, 57(2), 1654–1664.

    Article  Google Scholar 

  • Kori, K., Pedaste, M., Tõnisson, E., Palts, T., Altin, H., Rantsus, R., ... & Rüütmann, T. (2015). First-year dropout in ICT studies. In Proceedings of the 2015 I.E. Global Engineering Education Conference (pp. 437–445). IEEE Press.

  • Lee, A. (2015). Determining the effects of computer science education at the secondary level on STEM major choices in postsecondary institutions in the United States. Computers & Education, 88, 241–255.

    Article  Google Scholar 

  • Lent, R. W., & Brown, S. D. (2006). On conceptualizing and assessing social cognitive constructs in career research: A measurement guide. Journal of Career Assessment, 14(1), 12–35.

    Article  Google Scholar 

  • Lewis, C. M., Yasuhara, K., & Anderson, R. E. (2011). Deciding to major in computer science: A grounded theory of students’ self-assessment of ability. In Proceedings of the Seventh International Workshop on Computing Education Research (pp. 3–10). ACM Press.

  • Lewis, C. M., Anderson, R. E., & Yasuhara, K. (2016). I don’t code all day: Fitting in computer science when the stereotypes don't fit. In Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 23–32). ACM Press.

  • Li, Q., Swaminathan, H., & Tang, J. (2009). Development of a classification system for engineering student characteristics affecting college enrollment and retention. Journal of Engineering Education, 98(4), 361–376.

    Article  Google Scholar 

  • Litzler, E., & Young, J. (2012). Understanding the risk of attrition in undergraduate engineering: Results from the project to assess climate in engineering. Journal of Engineering Education, 101(2), 319–345.

    Article  Google Scholar 

  • Malik, S. I., & Coldwell-Neilson, J. (2016). A model for teaching an introductory programming course using ADRI. Education and Information Technologies, (first online) 1–32. doi:10.1007/s10639–016–9474-0

  • Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among US students. Science Education, 95(5), 877–907.

    Article  Google Scholar 

  • Marra, R. M., Rodgers, K. A., Shen, D., & Bogue, B. (2012). Leaving engineering: A multi-year single institution study. Journal of Engineering Education, 101(1), 6–27.

    Article  Google Scholar 

  • Masnick, A. M., Valenti, S. S., Cox, B. D., & Osman, C. J. (2010). A multidimensional scaling analysis of students’ attitudes about science careers. International Journal of Science Education, 32(5), 653–667.

    Article  Google Scholar 

  • Mau, W. C. (2003). Factors that influence persistence in science and engineering career aspirations. Career Development Quarterly, 51(3), 234–243.

    Article  Google Scholar 

  • Morrison, B. B., & Preston, J. A. (2009). Engagement: gaming throughout the curriculum. In ACM SIGCSE Bulletin, 41 (1), pp. 342–346. ACM Press.

  • Morton, E. (2005). Beyond the barriers: What women want in IT. Retrieved 23 Feb. 2016 from: http://BuilderAU.com.au.

  • Nachtigall, C., Kroehne, U., Funke, F., & Steyer, R. (2003). Pros and cons of structural equation modeling. Methods Psychological Research Online, 8(2), 1–22.

    Google Scholar 

  • O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690.

    Article  Google Scholar 

  • Ohland, M., Sheppard, S., Lichtenstein, G., Eris, O., Chachra, D., & Layton, R. (2008). Persistence, engagement, and migration in engineering programs. Journal of Engineering Education, 97(3), 259–278.

    Article  Google Scholar 

  • Pappas, I., Giannakos, M., & Jaccheri, L. (2016). Investigating factors influencing students’ intention to dropout computer science studies. In Proceedings of the 2016 ACM Annual Conference on Innovation and Technology in Computer Science Education. 198–203, ACM Press.

  • Pereira, F. A. M., Ramos, A. S. M., Gouvêa, M. A., & da Costa, M. F. (2015). Satisfaction and continuous use intention of e-learning service in Brazilian public organizations. Computers in Human Behavior, 46, 139–148.

    Article  Google Scholar 

  • Pike, G. R., Kuh, G. D., McCormick, A. C., Ethington, C. A., & Smart, J. C. (2011). If and when money matters: The relationships among educational expenditures, student engagement and students’ learning outcomes. Research in Higher Education, 52(1), 81–106.

    Article  Google Scholar 

  • Pirker, J., Riffnaller-Schiefer, M., & Gütl, C. (2014). Motivational active learning: Engaging university students in computer science education. In Proceedings of the 2014 Conference on Innovation & Technology in Computer Science Education (pp. 297–302), ACM Press.

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Article  Google Scholar 

  • Rodgers, K. A., & Summers, J. J. (2008). African American students at predominantly White institutions: A motivational and self-systems approach to understanding retention. Educational Psychology Review, 20(2), 171–190.

    Article  Google Scholar 

  • Rosson, M. B., Carroll, J. M., & Sinha, H. (2011). Orientation of undergraduates toward careers in the Computer and Information Sciences: Gender, self-efficacy and social support. ACM Transactions on Computing Education, 11(3), 14.

    Article  Google Scholar 

  • Seymour, E., & Hewitt, N. (1997). Talking about leaving: Why undergraduates leave the sciences. Boulder, CO: Westview Press.

    Google Scholar 

  • Stout, J., & Tamer, B. (2016). Collaborative learning eliminates the negative impact of gender stereotypes on women's self-concept. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (pp. 496–496). ACM Press.

  • Suresh, R. (2006). The relationship between barrier courses and persistence in engineering. Journal of College Student Retention, 8(2), 215–239.

    Article  Google Scholar 

  • Tan, P. J. B. (2015). English e-learning in the virtual classroom and the factors that influence ESL (English as a second language): Taiwanese citizens’ acceptance and use of the modular object-oriented dynamic learning environment. Social Science Information, 54(2), 221–228.

    Article  Google Scholar 

  • Toutkoushian, R. K., & Smart, J. C. (2001). Do institutional characteristics affect student gains from college? The Review of Higher Education, 25(1), 39–61.

    Article  Google Scholar 

  • U.S. Bureau of Labor Statistics (BLS). (2014). Employment projections 2010–2020. Retrieved 25 Feb. 2016 from: http://www.bls.gov/emp/

  • Vogt, C. M. (2008). Faculty as a critical juncture in student retention and performance in engineering programs. Journal of Engineering Education, 97(1), 27–36.

    Article  Google Scholar 

  • Walden, S. E., & Foor, C. (2008). What’s to keep you from dropping out? Student immigration into and within engineering. Journal of Engineering Education, 97(2), 191–205.

    Article  Google Scholar 

  • Watt, H. M., Shapka, J. D., Morris, Z. A., Durik, A. M., Keating, D. P., & Eccles, J. S. (2012). Gendered motivational processes affecting high school mathematics participation, educational aspirations, and career plans: A comparison of samples from Australia, Canada, and the United States. Developmental Psychology, 48(6), 1594–1611.

    Article  Google Scholar 

  • Weng, F., Cheong, F., & Cheong, C. (2010). The combined effect of self-efficacy and academic integration on higher education students studying IT majors in Taiwan. Education and Information Technologies, 15(4), 333–353.

    Article  Google Scholar 

  • Xenos, M., Pierrakeas, C., & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University. Computers & Education, 39(4), 361–377.

    Article  Google Scholar 

  • Xu, Y. J. (2013). Career outcomes of STEM and non-STEM college graduates: persistence in majored-field and influential factors in career choices. Research in Higher Education, 54(3), 349–382.

    Article  Google Scholar 

  • Zwedin, S. (2014). Computing degrees and enrollment trends: From the 2012–2014 CRA Talbee Survey. Computing Research Association: Washington D.C.

    Google Scholar 

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Acknowledgments

The authors would like to thank all the students at the Department of Computer and Information Science of NTNU that took part and responded in this study. This work was funded by the Norwegian Research Council under the projects FUTURE LEARNING (number: 255129/H20). This work was carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship Programme.

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Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Sem Sælands vei 7-9, 7491, Trondheim, Norway

    Michail N. Giannakos, Ilias O. Pappas & Letizia Jaccheri

  2. School of Education, Curtin University, Bentley Campus, Building 501, Level 3, Kent Street, Perth, WA, 6102, Australia

    Demetrios G. Sampson

Authors
  1. Michail N. Giannakos
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  2. Ilias O. Pappas
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  3. Letizia Jaccheri
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  4. Demetrios G. Sampson
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Correspondence to Ilias O. Pappas.

Appendix

Appendix

Table 4 Scale items with mean, standard deviation, and standardized loading
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Giannakos, M.N., Pappas, I.O., Jaccheri, L. et al. Understanding student retention in computer science education: The role of environment, gains, barriers and usefulness. Educ Inf Technol 22, 2365–2382 (2017). https://doi.org/10.1007/s10639-016-9538-1

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Keywords

  • Retention
  • Computer science education
  • Higher education
  • Dropout