skip to main content
research-article

Identifying Pathways to Computer Science: The Long-Term Impact of Short-Term Game Programming Outreach Interventions

Published:16 January 2019Publication History
Skip Abstract Section

Abstract

Short-term outreach interventions are conducted to raise young students’ awareness of the computer science (CS) field. Typically, these interventions are targeted at K–12 students, attempting to encourage them to study CS in higher education. This study is based on a series of extra-curricular outreach events that introduced students to the discipline of computing, nurturing creative computational thinking through problem solving and game programming. To assess the long-term impact of this campaign, the participants were contacted and interviewed two to five years after they had attended an outreach event. We studied how participating in the outreach program affected the students’ perceptions of CS as a field and, more importantly, how it affected their educational choices. We found that the outreach program generally had a positive effect on the students’ educational choices. The most prominent finding was that students who already possessed a “maintained situational interest” in CS found that the event strengthened their confidence in studying CS. However, many students were not affected by attending the program, but their perceptions of CS did change. Our results emphasize the need to provide continuing possibilities for interested students to experiment with computing-related activities and hence maintain their emerging individual interests.

References

  1. Mete Akcaoglu and Matthew J. Koehler. 2014. Cognitive outcomes from the game-design and learning (GDL) after-school program. Comput. Educ. 75 (2014), 72--81.Google ScholarGoogle ScholarCross RefCross Ref
  2. Mohammed Al-Bow, Debra Austin, Jeffrey Edgington, Rafael Fajardo, Joshua Fishburn, Carlos Lara, Scott Leutenegger, and Susan Meyer. 2009. Using game creation for teaching computer programming to high school students and teachers. In Proceedings of the 14th Annual ACM SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE’09). ACM, New York, NY, 104--108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Vicki L. Almstrum. 2003. What is the attraction to computing?Commun. ACM 46, 9 (Sept. 2003), 51--55. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Neil Anderson, Colin Lankshear, Carolyn Timms, and Lyn Courtney. 2008. “Because it’s boring, irrelevant, and I don’t like computers”: Why high school girls avoid professionally-oriented ICT subjects. Comput. Educ. 50, 4 (May 2008), 1304--1318. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Michal Armoni, Orni Meerbaum-Salant, and Mordechai Ben-Ari. 2015. From scratch to “real” programming. Trans. Comput. Educ. 14, 4, Article 25 (Feb. 2015), 15 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Tehmina Basit. 2003. Manual or electronic? The role of coding in qualitative data analysis. Educ. Res. 45, 2 (1 June 2003), 143--154.Google ScholarGoogle Scholar
  7. Ahmet Baytak. 2009. An Investigation of the Artifacts, Outcomes, and Processes of Constructing Computer Games about Environmental Science in a Fifth Grade Science Classroom. Ph.D. Dissertation. The Pennsylvania State University.Google ScholarGoogle Scholar
  8. Courtney K. Blackwell, Alexis R. Lauricella, and Ellen Wartella. 2014. Factors influencing digital technology use in early childhood education. Comput. Edu. 77 (2014), 82--90.Google ScholarGoogle ScholarCross RefCross Ref
  9. Neil C. C. Brown, Sue Sentance, Tom Crick, and Simon Humphreys. 2014. Restart: The resurgence of computer science in UK schools. Trans. Comput. Educ. 14, 2 (June 2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Lori Carter. 2006. Why students with an apparent aptitude for computer science don’t choose to major in computer science. SIGCSE Bull. 38, 1 (Mar. 2006), 27--31. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Anna Cleaves. 2005. The formation of science choices in secondary school. Int. J. Sci. Educ. 27, 4 (1 Jan. 2005), 471--486.Google ScholarGoogle ScholarCross RefCross Ref
  12. John W. Creswell and Vicki L. Plano Clark. 2011. Designing and Conducting Mixed Methods Research (2nd ed.). Sage Publications, London.Google ScholarGoogle Scholar
  13. Mihaly Csikszentmihalyi. 1991. Flow: The Psychology of Optimal Experience. Vol. 41. Harper Perennial.Google ScholarGoogle Scholar
  14. Shanna R. Daly, Erika A. Mosyjowski, and Colleen M. Seifert. 2014. Teaching creativity in engineering courses. J. Eng. Educ. 103, 3 (2014), 417--449.Google ScholarGoogle ScholarCross RefCross Ref
  15. Adrienne Decker and Monica M. McGill. 2017. Pre-college computing outreach research: Toward improving the practice. In Proceedings of the 2017 ACM SIGCSE technical symposium on computer science education (SIGCSE’17). ACM, New York, NY, 153--158. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Tim DeClue. 2009. A theory of attrition in computer science education which explores the effect of learning theory, gender, and context. J. Comput. Sci. Coll. 24, 5 (May 2009), 115--121. Retrieved from http://portal.acm.org/citation.cfm?id=1516620. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Peggy Doerschuk, Jiangjiang Liu, and Judith Mann. 2011. INSPIRED high school computing academies. Trans. Comput. Educ. 11 (July 2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Mary A. Egan and Timoth Lederman. 2011. The impact of IMPACT: Assessing students’ perceptions after a day of computer exploration. In Proceedings of the 16th Annual Joint Conference on Innovation and Technology in Computer Science Education (ITiCSE’11). ACM, New York, NY, 318--322. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Leon Festinger. 1962. A Theory of Cognitive Dissonance. Vol. 2. Stanford University Press.Google ScholarGoogle Scholar
  20. Jennifer A. Fredricks, Phyllis C. Blumenfeld, and Alison H. Paris. 2004. School engagement: Potential of the concept, state of the evidence. Rev. Educ. Res. 74, 1 (01 Mar. 2004), 59--109.Google ScholarGoogle ScholarCross RefCross Ref
  21. Mariia Gavriushenko, Mirka Saarela, and Tommi Kärkkäinen. 2017. Supporting institutional awareness and academic advising using clustered study profiles. In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU’17). 35--46.Google ScholarGoogle ScholarCross RefCross Ref
  22. Rani George. 2000. Measuring change in students’ attitudes toward science over time: An application of latent variable growth modeling. J. Sci. Educ. Technol. 9, 3 (2000), 213--225.Google ScholarGoogle ScholarCross RefCross Ref
  23. Barney Glaser and Anselm Strauss. 1999. The Discovery of Grounded Theory: Strategies for Qualitative Research (8th ed.). Aldine Transaction.Google ScholarGoogle Scholar
  24. Mark Guzdial, Barbara Ericson, Tom McKlin, and Shelly Engelman. 2014. Georgia computes! An intervention in a U.S. state, with formal and informal education in a policy context. Trans. Comput. Educ. 14, 2 (June 2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Roxana Hadad. 2013. Using game design as a means to make computer science accessible to adolescents. Cases on Digital Game-Based Learning: Methods, Models, and Strategies (2013), 279--300.Google ScholarGoogle Scholar
  26. Margaret Hamilton, Andrew L. Reilly, Naomi Augar, Vanea Chiprianov, Eveling C. Gutierrez, Elizabeth V. Duarte, Helen H. Hu, Shoba Ittyipe, Janice L. Pearce, Michael Oudshoorn, and Emma Wong. 2016. Gender equity in computing: International faculty perceptions and current practices. In Proceedings of the 2016 ITiCSE Working Group Reports (ITiCSE’16). ACM, New York, NY, 81--102. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Judith M. Harackiewicz, Kenneth E. Barron, John M. Tauer, and Andrew J. Elliot. 2002. Predicting success in college: A longitudinal study of achievement goals and ability measures as predictors of interest and performance from freshman year through graduation.J. Educ. Psychol. 94, 3 (2002), 562--575.Google ScholarGoogle ScholarCross RefCross Ref
  28. Idit Harel Caperton. 2010. Toward a theory of game-media literacy: Playing and building as reading and writing. Int. J. Gam. Comput.-Med. Simul. 2, 1 (2010), 1--16.Google ScholarGoogle ScholarCross RefCross Ref
  29. A. Harriger, Alejandra J. Magana, and R. Lovan. 2012. Identifying the impact of the SPIRIT program in student knowledge, attitudes, and perceptions toward computing careers. In Proceedings of the Frontiers in Education Conference. IEEE, 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Curtis R. Henrie, Lisa R. Halverson, and Charles R. Graham. 2015. Measuring student engagement in technology-mediated learning: A review. Comput. Educ. 90 (2015), 36--53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Suzanne Hidi and K. Ann Renninger. 2006. The four-phase model of interest development. Educ. Psychol. 41, 2 (1 June 2006), 111--127.Google ScholarGoogle Scholar
  32. Henriette T. Holmegaard, Lars M. Ulriksen, and Lene M. Madsen. 2012. The process of choosing what to study: A longitudinal study of upper secondary students’ identity work when choosing higher education. Scand. J. Educ. Res. 58, 1 (21 June 2012), 21--40.Google ScholarGoogle Scholar
  33. Hsiu-Fang Hsieh and Sarah E. Shannon. 2005. Three approaches to qualitative content analysis. Qual. Health Res. 15, 9 (01 Nov. 2005), 1277--1288.Google ScholarGoogle Scholar
  34. Peter Hubwieser, Michail N. Giannakos, Marc Berges, Torsten Brinda, Ira Diethelm, Johannes Magenheim, Yogendra Pal, Jana Jackova, and Egle Jasute. 2015. A global snapshot of computer science education in K-12 schools. In Proceedings of the ITiCSE on Working Group Reports (ITICSE-WGR’15). ACM, New York, NY, 65--83. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Jung W. Hur, Carey E. Andrzejewski, and Daniela Marghitu. 2017. Girls and computer science: Experiences, perceptions, and career aspirations. Comput. Sci. Educ. (18 Sept. 2017), 1--21.Google ScholarGoogle Scholar
  36. Yasmin B. Kafai and Quinn Burke. 2015. Constructionist gaming: Understanding the benefits of making games for learning. Educ. Psychol. 50, 4 (2 Oct. 2015), 313--334.Google ScholarGoogle Scholar
  37. Yasmin B. Kafai, Quinn Burke, and Mitchel Resnick. 2014. Connected Code: Why Children Need to Learn Programming. MIT Press. Google ScholarGoogle ScholarCross RefCross Ref
  38. Alison Kelly. 1986. The development of girls’ and boys’ attitudes to science: A longitudinal study. Eur. J. Sci. Educ. 8, 4 (1 Oct. 1986), 399--412.Google ScholarGoogle ScholarCross RefCross Ref
  39. Melisa Koorsse, Charmain Cilliers, and André Calitz. 2015. Programming assistance tools to support the learning of IT programming in South African secondary schools. Comput. Educ. 82 (2015), 162--178. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Andreas Krapp and Manfred Prenzel. 2011. Research on interest in science: Theories, methods, and findings. Int. J. Sci. Educ. 33, 1 (2011), 27--50.Google ScholarGoogle ScholarCross RefCross Ref
  41. Antti-Jussi Lakanen and Ville Isomöttönen. 2015. What does it take to do computer programming?: Surveying the K-12 students’ conceptions. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE’15). ACM, New York, NY, 458--463. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Antti-Jussi Lakanen, Ville Isomöttönen, and Vesa Lappalainen. 2012. Life two years after a game programming course: Longitudinal viewpoints on K-12 outreach. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (SIGCSE’12). ACM, New York, NY, 481--486. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Antti-Jussi Lakanen, Ville Isomöttönen, and Vesa Lappalainen. 2014. Five years of game programming outreach: Understanding student differences. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (SIGCSE’14). ACM, New York, NY, 647--652. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. S. D. Lamborn, F. M. Newmann, and G. G. Wehlage. 1992. The Significance and Sources of Student Engagement. Teachers College Press, New York, 11--39.Google ScholarGoogle Scholar
  45. Winnie W. Y. Lau, Grace Ngai, Stephen C. F. Chan, and Joey C. Y. Cheung. 2009. Learning programming through fashion and design: A pilot summer course in wearable computing for middle school students. SIGCSE Bull. 41, 1 (Mar. 2009), 504--508. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Michael A. Lawson and Hal A. Lawson. 2013. New conceptual frameworks for student engagement research, policy, and practice. Rev. Educ. Res. (19 Mar. 2013), 432--479.Google ScholarGoogle Scholar
  47. R. Layer, Mark Sherriff, and L. Tychonievich. 2012. “Inform, experience, implement”—Teaching an intensive high school summer course. In Proceedings of the Frontiers in Education Conference (FIE’12). IEEE, 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Terry Lyons. 2006. Different countries, same science classes: Students’ experiences of school science in their own words. Int. J. Sci. Educ. 28, 6 (12 May 2006), 591--613.Google ScholarGoogle ScholarCross RefCross Ref
  49. Jane Margolis and Allan Fisher. 2003. Unlocking the Clubhouse: Women in Computing. MIT press.Google ScholarGoogle Scholar
  50. Bruce R. Maxim and Bruce S. Elenbogen. 2009. Attracting K-12 students to study computing. In Frontiers in Education Conference (FIE’09). IEEE Press, Piscataway, NJ, 119--123. Retrieved from http://dl.acm.org/citation.cfm?id=1733663.1733701. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Robert McCartney, Jonas Boustedt, Anna Eckerdal, Kate Sanders, Lynda Thomas, and Carol Zander. 2016. Why computing students learn on their own: Motivation for self-directed learning of computing. Trans. Comput. Educ. 16, 1, Article 2 (Jan. 2016), 18 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Monica M. McGill, Adrienne Decker, and Amber Settle. 2015. Does outreach impact choices of major for underrepresented undergraduate students? In Proceedings of the 11th Annual International Conference on International Computing Education Research (ICER’15). ACM, New York, NY, 71--80. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Monica M. McGill, Adrienne Decker, and Amber Settle. 2016. Undergraduate students' perceptions of the impact of pre-college computing activities on choices of major. Trans. Comput. Educ. 16, 4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Barbara M. Moskal, Catherine Skokan, Laura Kosbar, Agata Dean, Caron Westland, Heidi Barker, Que N. Nguyen, and Jennifer Tafoya. 2007. K-12 outreach: Identifying the broader impacts of four outreach projects. J. Eng. Educ. 96, 3 (1 July 2007), 173--189.Google ScholarGoogle ScholarCross RefCross Ref
  55. Yunusa Olufadi. 2015. A configurational approach to the investigation of the multiple paths to success of students through mobile phone use behaviors. Comput. Educ. 86 (2015), 84--104. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Opetushallitus. 2014. Perusopetuksen opetussuunnitelman perusteet. Retrieved from https://www.oph.fi/saadokset_ja_ohjeet/opetussuunnitelmien_ja_tutkintojen_perusteet/perusopetus.Google ScholarGoogle Scholar
  57. Jonathan Osborne and Sue Collins. 2001. Pupils’ views of the role and value of the science curriculum: A focus-group study. Int. J. Sci. Educ. 23, 5 (1 May 2001), 441--467.Google ScholarGoogle ScholarCross RefCross Ref
  58. Aura Paloheimo, Kaisa Pohjonen, and Pirjo Putila. 2011. Women and higher engineering education — Choosing one’s degree program. In Proceedings of the Frontiers in Education Conference (FIE’11). IEEE, T2H--1--T2H--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Marina Papastergiou. 2008. Are computer science and information technology still masculine fields? High school studentsâ perceptions and career choices. Comput. Educ. 51, 2 (2008), 594--608. Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Michael Q. Patton. 2002. Qualitative Research and Evaluation Methods (2 ed.). Sage Publications, London, UK. Retrieved from http://www.amazon.com/Qualitative-Research-Evaluation-Methods-Michael/dp/0761919716.Google ScholarGoogle Scholar
  61. K. Ann Renninger and Suzanne Hidi. 2011. Revisiting the conceptualization, measurement, and generation of interest. Educ. Psychol. 46, 3 (1 July 2011), 168--184.Google ScholarGoogle Scholar
  62. Alexander Repenning, David Webb, and Andri Ioannidou. 2010. Scalable game design and the development of a checklist for getting computational thinking into public schools. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (SIGCSE’10). ACM, New York, NY, 265--269. Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Judy Robertson. 2013. The influence of a game-making project on male and female learners’ attitudes to computing. Comput. Sci. Educ. 23, 1 (2013), 58--83.Google ScholarGoogle ScholarCross RefCross Ref
  64. Mary Beth Rosson, John M. Carroll, and Hansa Sinha. 2011. Orientation of undergraduates toward careers in the computer and information sciences: Gender, self-efficacy and social support. Trans. Comput. Educ. 11, 3, Article 14 (Oct. 2011), 23 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. Miguel A. Rubio, Rocio Romero-Zaliz, Carolina Mañoso, and Angel P. de Madrid. 2015. Closing the gender gap in an introductory programming course. Comput. Educ. 82 (2015), 409--420. Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. Mirka Saarela and Tommi Kärkkäinen. 2017. Knowledge discovery from the programme for international student assessment. In Learning Analytics: Fundaments, Applications, and Trends. Springer, 229--267.Google ScholarGoogle Scholar
  67. Carol Sansone and Jessi L. Smith. 2000. Interest and Self-Regulation: The Relation Between Having To and Wanting To. Academic Press, San Diego, 341--372.Google ScholarGoogle Scholar
  68. Pasqueline D. Scaico, Ruy José, and José Jorge Lima Dias. 2017. Analyzing how interest in learning programming changes during a CS0 course: A qualitative study with brazilian undergraduates. In Proceedings of the ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE’17). ACM, New York, NY, 16--21. Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. Pratim Sengupta, John S. Kinnebrew, Satabdi Basu, Gautam Biswas, and Douglas Clark. 2013. Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies 18, 2 (2013), 351--380. Google ScholarGoogle ScholarCross RefCross Ref
  70. Elaine Seymour and Nancy M. Hewitt. 1997. Talking about Leaving: Why Undergraduates Leave the Sciences. Westview Press, Oxford, UK.Google ScholarGoogle Scholar
  71. David J. Shernoff, Mihaly Csikszentmihalyi, Barbara Shneider, and Elisa S. Shernoff. 2003. Student engagement in high school classrooms from the perspective of flow theory. School Psychol. Quart. 18, 2 (2003), 158--176.Google ScholarGoogle ScholarCross RefCross Ref
  72. Antoine van den Beemt and Isabelle Diepstraten. 2016. Teacher perspectives on ICT: A learning ecology approach. Comput. Educ. 92--93 (2016), 161--170. Google ScholarGoogle ScholarDigital LibraryDigital Library
  73. Jennifer Wang, Hai Hong, Jason Ravitz, and Marielena Ivory. 2015. Gender differences in factors influencing pursuit of computer science and related fields. In Proceedings of the ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE’15). ACM, New York, NY, 117--122. Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Jeannette M. Wing. 2006. Computational thinking. Commun. ACM 49, 3 (2006), 33--35. Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. Brian E. Woolnough. 1996. Changing pupils’ attitudes to careers in science. Phys. Educ. 31, 5 (1996), 301. Retrieved from http://stacks.iop.org/0031-9120/31/i=5/a=020.Google ScholarGoogle ScholarCross RefCross Ref
  76. Sarita Yardi and Amy Bruckman. 2007. What is computing?: Bridging the gap between teenagers’ perceptions and graduate students’ experiences. In Proceedings of the 3rd International Workshop on Computing Education Research (ICER’07). ACM, New York, NY, 39--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  77. David S. Yeager, Marlone D. Henderson, David Paunesku, Gregory M. Walton, Sidney DâMello, Brian J. Spitzer, and Angela Lee Duckworth. 2014. Boring but important: A self-transcendent purpose for learning fosters academic self-regulation.J. Personal. Social Psychol. 107, 4 (2014), 559.Google ScholarGoogle ScholarCross RefCross Ref
  78. Barry J. Zimmerman, Albert Bandura, and Manuel Martinez-Pons. 1992. Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. Amer. Edu. Res. J. 29, 3 (1992), 663--676. Retrieved from http://www.jstor.org/stable/1163261.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Identifying Pathways to Computer Science: The Long-Term Impact of Short-Term Game Programming Outreach Interventions

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Computing Education
      ACM Transactions on Computing Education  Volume 19, Issue 3
      September 2019
      333 pages
      EISSN:1946-6226
      DOI:10.1145/3308443
      Issue’s Table of Contents

      Copyright © 2019 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 16 January 2019
      • Accepted: 1 September 2018
      • Revised: 1 August 2018
      • Received: 1 December 2016
      Published in toce Volume 19, Issue 3

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format