Abstract
Computer science education, including computational thinking, has received considerable attention over the last few years as more and more countries are expanding or starting efforts in the primary and secondary schools. In this chapter, we provide examples of computer science efforts in a number of countries, including the United States, and discuss how these efforts to increase the role of computing in schools gives us a unique opportunity to expand computing education research, which has significantly lagged the rapid growth of computer science. We have laid out directions for future research under two broad areas of teaching training and student learning. Specifically, we discuss potential research areas around knowledge teachers need to teach computing ideas and factors that influence students learning to program.
“The child programs the computer and, in doing so, both acquires a sense of mastery over a piece of the most modern and powerful technology and establishes an intimate contact with some of the deepest ideas from science, from mathematics, and from the art of intellectual model building” – Seymour Papert
References
Barker, R. J., & Unger, E. a. (1983). A predictor for success in an introductory programming class based upon abstract reasoning development. ACM SIGCSE Bulletin, 15(1), 154–158.
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54. https://doi.org/10.1145/1929887.1929905
Bell, T., Andreae, P., & Lambert, L. (2010). Computer science in New Zealand high schools. In Proceedings of the twelfth Australasian conference on computing education (Vol. 103, pp. 15–22). Brisbane, Australia: Australian Computer Society.
Bergin, S., & Reilly, R. (2005). Programming: Factors that influence success. ACM SIGCSE Bulletin, 37(1), 411–415.
Bergin, S., Reilly, R., & Traynor, D. (2005). Examining the role of self-regulated learning on introductory programming performance. Proceedings of the first international workshop on computing education research (pp. 81–86).
Bureau of Labor Statistics, U.S. Department of Labor. (2016). Occupational Outlook Handbook, 2016–17 Edition, Software Developers. Retrieved from https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm
Calao, L. A., Moreno-Leon, J., Correa, H. E., & Robles, G. (2015). Developing mathematical thinking with scratch an experiment with 6th grade students. In Design for teaching and learning in a networked world (pp. 17–27). Springer International Publishing.
CAS. (2016). Computing science teachers in Scotland. Retrieved from http://www.cas.scot/wp-content/ uploads/2016/08/ComputingTeachersinScotland-CASSReport2016.pdf.
Cooper, S., Forbes, J., Fox, A., Hambrusch, S., & Ko, A. (2016). The importance of computing education research. Retrieved from http://cra.org/ccc/wp-content/uploads/sites/2/2015/01/CSEdResearchWhitePaper2016.pdf
Csizmadia, A., Curzon, P., Dorling, M., Humphreys, S., Ng, T., Selby, C., & Woollard, J. (2015). Computational thinking a guide for teachers. Retrieved from: https://community.computingatschool.org.uk/resources/2324/single
CSTA. (2013). Bugs in the system: Computer science teacher certification in the U.S. New York. Retrieved from http://csta.acm.org/ComputerScienceTeacherCertification/sub/CSTA_ BugsInTheSystem.pdf
Denning, P. J. (2017). Computational thinking in science. American Scientist, 105(1), 13. https://doi.org/10.1511/2017.124.13
Ericson, B. (2016). Detailed race and gender information 2016. Retrieved from http://home.cc.gatech.edu/ice-gt/596
Escherle, N. A., Ramirez-Ramirez, S. I., Basawapatna, A. R., Assaf, D., Repenning, A., Maiello, C., … Nolazco-Flores, J. A. (2016). Piloting computer science education week in Mexico. In Proceedings of the 47th ACM technical symposium on computing science education (pp. 431–436). Memphis, Tennessee: ACM.
Fantilli, R. D., & McDougall, D. E. (2009). A study of novice teachers: Challenges and supports in the first years. Teaching & Teacher Education, 25(6), 814–825.
Flatland, R., Lim, D., Matthews, J., & Vandenberg, S. (2015). Supporting CS10K: A new computer science methods course for mathematics education students. In Proceedings of the 46th ACM technical symposium on computer science education (pp. 302–307). Kansas City, Missouri: ACM.
Furber, S. (2012). Shut down or restart? The way forward for computing in UK schools. London: The Royal Society.
Gal-Ezer, J., & Stephenson, C. (2014). A tale of two countries: Successes and challenges in K-12 computer science education in Israel and the United States. ACM Transactions on Computing Education (TOCE), 14(2), 8.
Gretter, S., & Yadav, A. (2016). Computational thinking and media & information literacy: An integrated approach to teaching twenty-first century skills. TechTrends, 60, 510–516. https://doi.org/10.1007/s11528-016-0098-4.
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
Guarino, C. M., Santibanez, L., & Daley, G. A. (2006). Teacher recruitment and retention: A review of the recent empirical literature. Review of Educational Research, 76(2), 173–208.
Hagan, D., & Markham, S. (2000). Does it help to have some programming experience before beginning a computing degree program? ACM SIGCSE Bulletin, 32(3), 25–28. ACM.
Hostetler, T. R., & Global. (1983). Predicting student success. SIGCSE Bulletin, 15, 40–44.
Ingersoll, R. (2001). Teacher turnover and teacher shortages: An organizational analysis. American Educational Research Journal, 38(3), 499–534.
Lenstra, J. K., Barthel, P., de Brock, E. O., de Jong, F. M. G., Lagendijk, R. L., Oortmerssen, G. v., et al. (2012). Digitale geletterdheid in het voortgezet onderwijs; Vaardigheden en atti- tudes voor de 21ste eeuw (Digital literacy in secondary education; Skills and attitudes for the 21st century). Amsterdam: KNAW.
Lishinski, A., Yadav, A., Good, J., and Enbody, R. (2016). Learning to program: Gender differences and interactive effects of students’ motivation, goals, and self-efficacy on performance. In Proceedings of international computing education research (ICER ’16) (pp. 211–220). Melbourne, Australia.
Margaritis, M., Magenheim, J., Hubwieser, P., Berges, M., Ohrndorf, L., & Schubert, S. (2015). Development of a competency model for computer science teachers at secondary school level. In IEEE global engineering education conference (EDUCON) (pp. 211–220). Tallinn: IEEE.
Mishra, P., & Yadav, A. (2013). Of art and algorithm: Rethinking technology & creativity in the 21st century. TechTrends, 57(3), 10–14. https://doi.org/10.1007/s11528-013-0668-7
Morrison, B. (2015). Computer science is different! Educational Psychology experiments do not reliably replicate in programming domain. Proceedings of the eleventh annual international conference on international computing education research (ICER ’15) (pp. 267–268). Omaha, Nebraska.
Papert, S. (1993). Mindstorms: Children, computers and powerful ideas. New York: Basic Books.
Peyton-Jones, S., Mitchell, B., & Humphreys, S. (2013). Computing at school in the UK: From guerrilla to gorilla. Retrieved from: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/07/ComputingAtSchoolCACM.pdf.
Ramalingam, V., LaBelle, D., & Wiedenbeck, S. (2004). Self-efficacy and mental models in learning to program. Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education – ITISCE ‘04 (171–175).
Robins, A. (2010). Learning edge momentum: A new account of outcomes in CS1. Computer Science Education, 20(1), 37–71.
Sarabia, A. (2015, Sept 21). Australia will teach primary students computer coding. Retrieved from http://www.pbs.org/newshour/rundown/australia-will-teach-primary-students-coding/
Sentance, S., & Csizmadia, A. (2016). Computing in the curriculum: Challenges and strategies from a teacher’s perspective. Education and Information Technologies, 1–27. https://doi.org/10.1007/s10639-016-9482-0.
Sentance, S., Humphreys, S., & Dorling, M. (2014). The network of teaching excellence in computer science and master teachers. In Proceedings of the 9th Workshop in Primary and Secondary Computing Education (WiPSCE ’14) (pp. 80–88). New York, NY, USA: ACM. https://doi.org/10.1145/2670757.2670789.
Strickland, D. (2014). L.A. Unified announces larger focus on computer science for K-12. Retrieved from: https://home.lausd.net/apps/news/article/407400.
Taylor, K., & Miller, C. C. (2015). De Blasio to announce 10-year deadline to offer computer science to all students. The New York Times. Retrieved from https://www.nytimes.com/2015/09/16/nyregion/de-blasio-to-announce-10-year-deadline-to-offer-computer-science-to-all-students.html?_r=0.
The College Board. (2016). AP computer science principles curriculum framework. Retrieved from https://advancesinap.collegeboard.org/stem/computer-science-principles/course-details
United Kingdom Department for Education. (2016, Oct 13). GCSE and equivalent results: 2015 to 2016 (provisional) [Data file]. Retrieved from https://www.gov.uk/government/statistics/gcse-and-equivalent-results-2015-to-2016-provisional.
Valentine, D. W. (2004). CS educational research: a meta-analysis of SIGCSE technical symposium proceedings. In SIGCSE (pp. 255–259). Norfolk. https://doi.org/10.1145/1028174.971391.
Voogt, J., & ten Brummelhuis, A. (2014). Information literacy in the Netherlands: Rise, fall and revival. In Reflections on the history of computers in education (pp. 83–93). Berlin/Heidelberg: Springer.
Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715–728. https://doi.org/10.1007/s10639-015-9412-6.
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5
Wiedenbeck, S. (2005). Factors affecting the success of non-majors in learning to program. Proceedings of the 2005 international workshop on Computing education research – ICER ’05 (pp. 13–24). Seattle, WA.
Wiedenbeck, S., LaBelle, D., & Kain, V. (2004). Factors affecting course outcomes in introductory programming. Proceedings of the 16th workshop of the psychology of programming interest group, (April):97–110. Carlow, Ireland.
Wilson, B. C., & Shrock, S. (2001). Contributing to success in an introductory computer science course: A study of twelve factors. ACM SIGCSE Bulletin, 33(1), 184–188.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725. https://doi.org/10.1098/rsta.2008.0118
Yadav A., & Cooper, S. (2017). Fostering creativity through computing. Communications of the ACM, 60(2), 31–33. https://doi.org/10.1145/3029595
Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S., & Korb, J. T. (2011). Introducing computational thinking in education courses. In 42nd ACM technical symposium on computer science education (pp. 465–470). Dallas, TX. https://doi.org/10.1145/1953163.1953297
Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 1–16.
Yadav, A., Gretter, S., Hambrusch, S., & Sands, P. (2016a). Expanding computer science education in schools: Understanding teacher experiences and challenges. Computer Science Education, 26, 235. https://doi.org/10.1080/08993408.2016.1257418
Yadav, A., Hong, H., & Stephenson, C. (2016b). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 1–4. https://doi.org/10.1007/s11528-016-0087-7
Yadav, A., Good, J., Voogt, J., & Fisser, P. (2017). Computational thinking as an emerging competence domain. In M. Mulder (Ed.), Competence-based vocational and professional education: Bridging the worlds of work and education (pp. 1051–1067). Cham: Springer. https://doi.org/10.1007/978-3-319-41713-4_49
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this entry
Cite this entry
Yadav, A., Sands, P., Good, J., Lishinki, A. (2018). Computer Science and Computational Thinking in the Curriculum: Research and Practice. In: Voogt, J., Knezek, G., Christensen, R., Lai, KW. (eds) Second Handbook of Information Technology in Primary and Secondary Education . Springer International Handbooks of Education. Springer, Cham. https://doi.org/10.1007/978-3-319-53803-7_6-2
Download citation
DOI: https://doi.org/10.1007/978-3-319-53803-7_6-2
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53803-7
Online ISBN: 978-3-319-53803-7
eBook Packages: Springer Reference EducationReference Module Humanities and Social SciencesReference Module Education
Publish with us
Chapter history
-
Latest
Computer Science and Computational Thinking in the Curriculum: Research and Practice- Published:
- 21 February 2018
DOI: https://doi.org/10.1007/978-3-319-53803-7_6-2
-
Original
Computer Science and Computational Thinking in the Curriculum: Research and Practice- Published:
- 12 January 2018
DOI: https://doi.org/10.1007/978-3-319-53803-7_6-1