Good (and Bad) Reasons to Teach All Students Computer Science

  • Colleen M. LewisEmail author


Recently everyone seems to be arguing that all students should learn computer science and/or learn to program. I agree. I see teaching all students computer science to be essential to counteracting our history and present state of differential access by race, class, and gender to computer science learning and computing-related jobs. However, teaching computer science is not a silver bullet or panacea. The content, assumptions, and implications of our arguments for teaching computer science matter. Some of the common arguments for why all students need to learn computer science are false; some do more to exclude than to expand participation in computing. This chapter seeks to deconstruct the many flawed reasons to teach all students computer science to help identify and amplify the good reasons.


Computer science Education CS4All Equity Computational thinking Programming Interdisciplinary 



This work was partially funded by National Science Foundation grant #1339404. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.


  1. Alvarado, C., Dodds, Z., & Libeskind-Hadas, R. (2012). Increasing women’s participation in computing at Harvey Mudd College. ACM Inroads, 3(4), 55–64.CrossRefGoogle Scholar
  2. Ashcraft, K. (2012). The glass slipper: ‘Incorporating’ occupational identity in management studies. Academy of Management Review.Google Scholar
  3. Ashcraft, K. L., & Ashcraft, C. (2015). Breaking the “glass slipper”: What diversity interventions can learn from the historical evolution of occupational identity in ICT and commercial aviation. In Connecting women (pp. 137–155). Springer International Publishing.Google Scholar
  4. Balliet, D., Li, N. P., Macfarlan, S. J., & Van Vugt, M. (2011). Sex differences in cooperation: A meta-analytic review of social dilemmas. Psychological Bulletin, 137(6), 881.CrossRefGoogle Scholar
  5. Barker, L. J., Garvin-Doxas, K., & Jackson, M. (2002, February). Defensive climate in the computer science classroom. ACM SIGCSE Bulletin, 34(1), 43–47 (ACM).Google Scholar
  6. Barker, L. J., McDowell, C., & Kalahar, K. (2009, March). Exploring factors that influence computer science introductory course students to persist in the major. ACM SIGCSE Bulletin, 41(1), 153–157 (ACM).Google Scholar
  7. Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn?: A taxonomy for far transfer. Psychological Bulletin, 128(4), 612.CrossRefGoogle Scholar
  8. Barocas, S. (2014). Data mining and the discourse on discrimination. In Data Ethics Workshop, Conference on Knowledge Discovery and Data Mining.Google Scholar
  9. Bobb, K. (2016, March 9). Why teaching computer science to students of color is vital to the future of our nation. The Root. Retrieved from
  10. Bose, D., Segui-Gomez, M., & Crandall, J. R. (2011). Vulnerability of female drivers involved in motor vehicle crashes: An analysis of US population at risk. American Journal of Public Health, 101(12), 2368–2373.CrossRefGoogle Scholar
  11. Camp, T. (2012). Computing, we have a problem…. ACM Inroads, 3(4), 34–40.CrossRefGoogle Scholar
  12. Ceci, S. J. (1991). How much does schooling influence general intelligence and its cognitive components? A reassessment of the evidence. Developmental Psychology, 27(5), 703.CrossRefGoogle Scholar
  13. Chachra, D. (2015, Jan 23). Why I am not a maker: When tech culture only celebrates creation, it risks ignoring those who teach, criticize, and take care of others. The Atlantic. Retrieved from
  14. Clements, D. H., Battista, M. T., & Sarama, J. (2001). Logo and geometry. Journal for Research in Mathematics Education. Monograph, 10, i–177.Google Scholar
  15. College Board. (2015). Program Summary Report 2015. Retrieved from
  16. Crawford, K. (2013). Think again: Big data. Foreign Policy, 9.Google Scholar
  17. Credé, M., Tynan, M. C., & Harms, P. D. (2016). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology.Google Scholar
  18. Cutts, Q., Cutts, E., Draper, S., O’Donnell, P., & Saffrey, P. (2010). Manipulating mindset to positively influence introductory programming performance. In Proceedings of the 41st ACM technical symposium on Computer science education (pp. 431–435). ACM.Google Scholar
  19. Denning, P. J. (2005). Is computer science science? Communications of the ACM, 48(4), 27–31.CrossRefGoogle Scholar
  20. diSessa, A. A. (2001). Changing minds: Computers, learning, and literacy. MIT Press.Google Scholar
  21. Dodds, Z., Libeskind-Hadas, R., Alvarado, C., & Kuenning, G. (2008). Evaluating a breadth-first CS 1 for scientists. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education, Portland, OR (pp. 266–270).Google Scholar
  22. Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087.CrossRefGoogle Scholar
  23. Dweck, C. S. (2008). Mindset: The new psychology of success. Random House Digital, Inc.Google Scholar
  24. Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), 256.CrossRefGoogle Scholar
  25. Ensmenger, N. L. (2012). The computer boys take over: Computers, programmers, and the politics of technical expertise. MIT Press.Google Scholar
  26. Garratt, P. (2016, May 6). How do algorithms perpetuate discrimination and what can we do to fix it? Retrieved from
  27. Golden, N. A. (2015). There’s still that window that’s open” the problem with “grit”. Urban Education, 1–28.Google Scholar
  28. Google for Education. (2014). Women who choose computer science—What really matters. Retrieved from
  29. Gutek, B. A., & Cohen, A. G. (1987). Sex ratios, sex role spillover, and sex at work: A comparison of men’s and women’s experiences. Human Relations, 40(2), 97–115.CrossRefGoogle Scholar
  30. Hampshire’s Community Advocacy Union. (n.d.). Hampshire Halloween Checklist: Is your costume racist? Retrieved from
  31. Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60(6), 581.CrossRefGoogle Scholar
  32. Ibarra, H., & Hansen, M. T. (2011). Are you a collaborative leader? Harvard Business Review, 89(7/8), 68–74.Google Scholar
  33. Jobs, S. (1995). Lost interview. Retrieved from
  34. Kafai, Y. B., & Burke, Q. (2014). Connected code: Why children need to learn programming. MIT Press.Google Scholar
  35. King, H. (2016, April 20). Snapchat’s new Bob Marley lens sparks ‘blackface’ outrage. CNN Money. Retrieved from
  36. Koschmann, T. (1997). Logo-as-latin redux. The Journal of the Learning Sciences, 6(4), 409–415.CrossRefGoogle Scholar
  37. Kurose, J. (2015). Booming undergraduate enrollments: A wave or a sea change? ACM Inroads, 6(4), 105–106.CrossRefGoogle Scholar
  38. Labaree, D. F. (1997). Public goods, private goods: The American struggle over educational goals. American Educational Research Journal, 34(1), 39–81.CrossRefGoogle Scholar
  39. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.Google Scholar
  40. Lewis, C. (2007). Attitudes and beliefs about computer science among students and faculty. ACM SIGCSE Bulletin, 39(2), 37–41.CrossRefGoogle Scholar
  41. Lewis, C. M. (2010, March). How programming environment shapes perception, learning and goals: Logo vs. scratch. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (pp. 346–350). ACM.Google Scholar
  42. 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 International Computer Science Education Research Workshop.Google Scholar
  43. Lewis, C. M., Esper, S., Bhattacharyya, V., Fa-Kaji, N., Dominguez, N., & Schlesinger, A. (2014). Children’s perceptions of what counts as a programming language. Journal of Computing Sciences in Colleges, 29(4), 123–133.Google Scholar
  44. Lewis, C. M., & Shah, N. (2012, February). Building upon and enriching grade four mathematics standards with programming curriculum. In Proceedings of the 43rd ACM technical symposium on Computer Science Education (pp. 57–62). ACM.Google Scholar
  45. Lewis, C. M., Titterton, N., & Clancy, M. (2012, September). Using collaboration to overcome disparities in Java experience. In Proceedings of the ninth annual international conference on International computing education research (pp. 79–86). ACM.Google Scholar
  46. 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.Google Scholar
  47. Margolis, J., Estrella, R., Goode, J., Jellison-Holme, J., & Nao, K. (2008). Stuck in the shallow end: Education, race, & computing. Cambridge, MA: MIT Press.Google Scholar
  48. Margolis, J., & Fisher, A. (2003). Unlocking the clubhouse: Women in computing. MIT press.Google Scholar
  49. Mariama-Aruthur, K. (2016, May 26). Dr. Kamau Bobb Talks Leadership and Diversity in STEM and Computer Science Education (Part I). Black Enterprise. Retrieved from
  50. McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Kolikant, Y. B. D., … Wilusz, T. (2001). A multi-national, multi-institutional study of assessment of programming skills of first-year CS students. ACM SIGCSE Bulletin, 33(4), 125–180.Google Scholar
  51. McGrath Cohoon, J. (2010). Harvey Mudd College’s Successful Systemic Approach (Case Study 2). Retrieved from
  52. McIntosh, P. (1989). White privilege: Unpacking the invisible knapsack. Independent School, 90(49), 2.Google Scholar
  53. National Center for Women in Information Technology. (2016, March 10). By the numbers. Retrieved from
  54. Nicol, A., Casey, C., & MacFarlane, S. (2002). Children are ready for speech technology-but is the technology ready for them. Interaction Design and Children, Eindhoven, The Netherlands.Google Scholar
  55. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.Google Scholar
  56. Papert, S., & Harel, I. (1991). Situating constructionism. Constructionism, 36, 1–11.Google Scholar
  57. Parker, M. C., & Guzdial, M. (2015, August). A critical research synthesis of privilege in computing education. In Research in Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT), 2015 (pp. 1–5). IEEE.Google Scholar
  58. Resnick, M. (2014). Forward. In Y. B. Kafia & Q. Burke (Eds.), Connected code: Why children need to learn programming (pp. xi–xiii). MIT Press.Google Scholar
  59. Roberts, E. S. (2011). Meeting the challenges of rising enrollments. ACM Inroads, 2(3), 4–6.CrossRefGoogle Scholar
  60. Robins, A. (2010). Learning edge momentum: A new account of outcomes in CS1. Computer Science Education, 20(1), 37–71.CrossRefGoogle Scholar
  61. Rodger, J. A., & Pendharkar, P. C. (2004). A field study of the impact of gender and user’s technical experience on the performance of voice-activated medical tracking application. International Journal of Human-Computer Studies, 60(5), 529–544.CrossRefGoogle Scholar
  62. Rose, A. (2010, January 22). Are face-detection cameras racist? TIME. Retrieved from,8599,1954643,00.html
  63. Schanzer, E., Fisler, K., Krishnamurthi, S., & Felleisen, M. (2015, February). Transferring skills at solving word problems from computing to algebra through bootstrap. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 616–621). ACM.Google Scholar
  64. Shaver, K. (2012, March 25). Female dummy makes her mark on male-dominated crash tests. The Washington Post. Retrieved from
  65. Simon, B., Hanks, B., Murphy, L., Fitzgerald, S., McCauley, R., Thomas, L., et al. (2008). Saying isn’t necessarily believing: Influencing self-theories in computing. In Proceedings of the Fourth International Workshop on Computing Education Research (pp. 173–184). ACM.Google Scholar
  66. Tatman, R. (2016, July 12). Google’s speech recognition has a gender bias. Making noise and hearing things.
  67. The coalition to diversify computing. (n.d.) Resources. Retrieved from
  68. Tricot, A., & Sweller, J. (2014). Domain-specific knowledge and why teaching generic skills does not work. Educational Psychology Review, 26(2), 265–283.CrossRefGoogle Scholar
  69. Vinsel, L. J. (2012, August 22). Why carmakers always insisted on male crash-test dummies. Bloomberg View. Retrieved from
  70. Wilensky, U., Brady, C. E., & Horn, M. S. (2014). Fostering computational literacy in science classrooms. Communications of the ACM, 57(8), 24–28.Google Scholar
  71. Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and technology, 8(1), 3–19.Google Scholar
  72. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.CrossRefGoogle Scholar
  73. Yeager, D., Walton, G., & Cohen, G. L. (2013). Addressing achievement gaps with psychological interventions. Phi Delta Kappan, 94(5), 62–65.Google Scholar
  74. Zhu, K. (2016, August 10). I’m deleting Snapchat, and you should too. Medium. Retrieved from

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Harvey Mudd CollegeClaremontUSA

Personalised recommendations