Integrating Computational Thinking in School Curriculum

• Ashcraft, C., & Blithe, S. (2009). Women in IT: The facts. Washington, DC: National Center for Women and Information Technology. Retrieved from http://www.ncwit.org/sites/default/files/legacy/pdf/NCWIT_TheFacts_rev2010.pdf

• Association for Computing Machinery. (2016). K–12 Computer Science framework. Retrieved from http://www.k12cs.org

• Astrachan, O., Cuny, J., Stephenson, C., & Wilson, C. (2011). The cs10k project: Mobilizing the community to transform high school computing. In Proceedings of the 42nd ACM Technical symposium on Computer Science Education (SIGCSE) (pp. 85–86). American Computing Machinery.

  Google Scholar 

• Augustine, N. R. (2005). Rising above the gathering storm: Energizing and employing America for a brighter economic future. Washington, DC: National Academies Press.

  Google Scholar 

• Bailey, D., & Borwein, J. M. (2011). Exploratory experimentation and computation. Notices of the American Mathematical Society, 58(10), 1410–1419.

  Google Scholar 

• 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.

  Article  Google Scholar 

• Basu, S., Sengupta, P., & Biswas, G. (2015). A scaffolding framework to support learning of emergent phenomena using multiagent based simulation environments. Research in Science Education, 45(2), 293–324. https://doi.org/10.1007/s11165-014-9424-z.

• Basu, S., Biswas, G., & Kinnebrew, J. S. (2017). Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment. User Modeling and User-Adapted Interaction, 27(1), 5–53.

  Google Scholar 

• Ben-Ari, M. (2004). Situated learning in computer science education. Computer Science Education, 14(2), 85–100. https://doi.org/10.1080/08993400412331363823.

  Article  Google Scholar 

• Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: A constructionist learning environment for materials science using agent-based modeling. International Journal of Computers for Mathematical Learning, 14(2), 81–119.

  Article  Google Scholar 

• Brennan, K., Valverde, A., Prempeh, J., Roque, R., & Chung, M. (2011). More than code: The significance of social interactions in young people's development as interactive media creators. In T. Bastiaens & M. Ebner (Eds.), Proceedings of world conference on educational multimedia, hypermedia and telecommunications (pp. 2147–2156). Chesapeake: AACE.

  Google Scholar 

• Buckingham, D. (2015). Do we really need media education 2.0? Teaching media in the age of participatory culture. In T. Lin, D. Chen, & V. Chai (Eds.), New media and learning in the 21st century (pp. 9–21). Singapore: Springer.

  Google Scholar 

• Burgstahler, S., Ladner, R., & Bellman, S. (2012). Strategies for increasing the participation in computing of students with disabilities. ACM Inroads, 3(4), 42–48.

  Article  Google Scholar 

• College Board. (2016). AP computer science principles curriculum framework 2016–2017. https://securemedia.collegeboard.org/digitalServices/pdf/ap/ap-computer-science-principles-curriculum-framework.pdf

• College Board. (2017). AP computer science principles. Retrieved from https://apcentral.collegeboard.org/pdf/apcomputer-science-principles-course-and-exam-description.pdf

• Computer Science Teacher Association Standards [CSTA] Task Force. (2011). K-12 Computer Science standards. Revised 2011. http://csta.acm.org/Curriculum/sub/K12Standards.html

• Computer Science Teachers Association [CSTA]. (2013). Bugs in the system: Computer science teacher certification in the U.S. New York: Author. Retrieved from http://csta.acm.org/ComputerScienceTeacherCertification/sub/CSTA_BugsInTheSystem.pdf

• Cuny, J., Snyder, L., & Wing, J. M. (2010). Demystifying computational thinking for non-computer scientists. Unpublished manuscript in progress, referenced in http://www.cs.cmu.edu/~CompThink/resources/TheLinkWing.pdf

• Cuny, J. (2012). Transforming high school computing: A call to action. ACM Inroads, 3, 32–36.

  Article  Google Scholar 

• Cuny, J. (2016, February). CS Education: Catching the Wave. Proceedings of the 47th ACM Technical Symposium on Computing Science Education. Association for Computing Machinery

  Google Scholar 

• Denning, P. J. (2017). Computational thinking in science. American Scientist, 105(1), 13–17. https://doi.org/10.1511/2017.124.

  Article  Google Scholar 

• Dickes, A., Sengupta, P., Farris, A. V., & Basu, S. (2016). Development of mechanistic reasoning and multi-level explanations in 3rd grade biology using multi-agent based models. Science Education. https://doi.org/10.1002/sce.21217.

• Emmott, S. (Ed.). (2006). Towards 2020 Science. Cambridge, UK: Microsoft Research.

  Google Scholar 

• Ericson, B., Armoni, M., Gal-Ezer, J., Seehorn, D., Stephenson, C., & Trees, F. (2008). Ensuring exemplary teaching in an essential discipline: Addressing the crisis in computer science teacher certification. Final report of the CSTA Teacher Certification Task Force. ACM.

  Google Scholar 

• Escherle, N. A., Ramirez-ramirez, S. I., Basawapatna, A. R., Maiello, C., & Nolazco-florez, J. A. (2016). Piloting computer science education week in Mexico. In Proceedings of the 47th ACM Technical Symposium on Computer Science Education (SIGCSE ‘16) (pp. 431–436). Memphis, TN. doi:https://doi.org/10.1145/2839509.2844598.

• Feurzeig, W., Papert, S., & Lawler, B. (2011). Programming-languages as a conceptual framework for teaching mathematics. Interactive Learning Environments, 19(5), 487–501.

  Article  Google Scholar 

• Furber, S. (2012). Shut down or restart? The way forward for computing in UK schools. Technical report. London: The Royal Society.

  Google Scholar 

• Gal-Ezer, J., & Stephenson, C. (2010). Computer science teacher preparation is critical. ACM Inroads, 1(1), 61–66.

  Article  Google Scholar 

• 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.

  Google Scholar 

• Gholipour, B. (2017). Discovery of 18 new autism-linked genes may point to new treatments. Scientific American, March.

  Google Scholar 

• Goode, J. (2011). Exploring computer science: An equity-based reform program for 21st century computing education. Journal for Computing Teachers. Retrieved from http://www.iste.org/store/magazines-and-journals/downloads/jct-downloads.aspx

• Goode, J., Margolis, J., & Chapman, G. (2014). Curriculum is not enough: The educational theory and research foundation of the exploring Computer Science professional development model. SIGCSE, 2014, 493–498.

  Google Scholar 

• Gootman, E. (2007, March 17). The critical years: For teachers, middle school is test of wills. New York Times.

  Google Scholar 

• Guzdial, M. (1995). Software-realized scaffolding to facilitate programming for science learning. Interactive Learning Environments, 4(1), 1–44.

  Article  Google Scholar 

• Guzdial, M. (2008). Education: Paving the way for computational thinking. Communications of the ACM, 51(8), 25–27. https://doi.org/10.1145/1378704.1378713.

  Article  Google Scholar 

• Hambrusch, S., Hoffmann, C., Korb, J. T., Haugan, M., & Hosking, A. L. (2009). A multidisciplinary approach towards computational thinking for science majors. ACM SIGCSE Bulletin, 41, 183–187.

  Article  Google Scholar 

• Jona, K., Wilensky, U., Trouille, L., Horn, M.S, Orton, K., Weintrop, D., & Beheshti, E. (2014). Embedding computational thinking in science, technology, engineering, and math (CT-STEM). Presented at the Future Directions in Computer Science Education Summit Meeting, Orlando.

  Google Scholar 

• Joyner, D. A., Goel, A. K., & Papin, N. (2014). MILA-S: Generation of agent-based simulations from conceptual models. In Proceedings of the 19th International conference on intelligent user interfaces (pp. 289–298). Haifa, Israel.

• Kemp, P., Wong, B., & Berry, M. (2016). The Roehampton annual computing education report 2015 data from England. Retrieved from https://drive.google.com/file/d/0B1xf_L-jCIzYZmZDbFAzb3BPUEk/view

• Khenner, E., & Semakin, I. (2014). School subject informatics (computer science) in Russia: Educational relevant areas. ACM Transactions on Computer Education, 14(2), 1–10. https://doi.org/10.1145/2602489.

  Article  Google Scholar 

• Kim, B., Pathak, S. A., Jacobson, M. J., Zhang, B., & Gobert, J. D. (2015a). Cycles of exploration, reflection, and consolidation in model-based learning of genetics. Journal of Science Education and Technology, 24(6), 789–802. https://doi.org/10.1007/s10956-015-9564-6.

  Article  Google Scholar 

• Kim, D. K., Jeong, D., Lu, L., Debnath, D., & Ming, H. (2015b). Opinions on computing education in Korean K-12 system: Higher education perspective. Journal of Computer Science Education, 25(4), 371–389.

  Article  Google Scholar 

• Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., et al. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting learning by design (tm) into practice. The Journal of the Learning Sciences, 12(4), 495–547.

  Article  Google Scholar 

• Lapidot, T., & Hazzan, O. (2003). Methods of teaching a computer science course for prospective teachers. Inroads – The Sigcse Bulletin, 35(4), 29–34.

  Article  Google Scholar 

• Lehrer, R., & Schauble, L. (2006). Cultivating model-based reasoning in science education. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 371–388). New York: Cambridge University Press.

  Google Scholar 

• Lin, C.C., Zhang, M., Beck, B., & Olsen, G. (2009). Embedding computer science concepts in K-12 science curricula. In Proceedings of the 40th ACM technical symposium on computer science education (pp 539–543). New York: ACM.

  Google Scholar 

• Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61.

  Article  Google Scholar 

• Margolis, J., & Goode, J. (2016). Ten lessons for CS for all. ACM Inroads Magazine, 7(4), 52–56.

  Google Scholar 

• Margolis, J., Goode, J., & Ryoo, J. (2014). Democratizing computer science knowledge. In educational leadership, STEM for all December 2014/January 2015 | Volume 72 | Number 4. p. 48–53.

  Google Scholar 

• Margolis, J., Ryoo, J. J., Sandoval, C. D. M., Lee, C., Goode, J., & Chapman, G. (2012). Beyond access: Broadening participation in high school computer science. ACM Inroads, 3(4), 72–78.

  Article  Google Scholar 

• Marling, C., Juedes, D. (2016). CS0 for computer science majors at Ohio University (pp. 138–143). New York: ACM. http://329Stocker Center.

• Mehalik, M. M., Doppelt, Y., & Schunn, C. D. (2008). Middle-school science through design-based learning versus scripted inquiry: Better overall science concept learning and equity gap reduction. Journal of Engineering Education, 97(1), 71–85.

  Article  Google Scholar 

• Menekse, M. (2015). Computer science teacher professional development in the United States: A review of studies published between 2004 and 2014. Computer Science Education, 25(4), 325–350.

  Article  Google Scholar 

• National Research Council. (2011a). Committee for the workshops on computational thinking: Report of a workshop of pedagogical aspects of computational thinking. Washington, DC: National Academies Press.

  Google Scholar 

• National Research Council. (2011b). Learning science through computer games and simulations. Washington, DC: The National Academies Press.

  Google Scholar 

• National Research Council. (2013). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.

  Google Scholar 

• National Science Foundation [NSF]. (2013). Broadening Participation in Computing Alliance Program (BPC-A). Retrieved on April 18, 2017, from https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503593

• National Science Foundation. (2016). Broadening Participation in Computing Alliance Program (BPC-A) Retrieved from https://nsf.gov/funding/pgm_summ.jsp?pims_id=503593&org=DIS&from_org=DIS

• Nersessian, N. J. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In R. N. Giere (Ed.), Cognitive models of science (pp. 3–45). Minneapolis: University of Minnesota Press.

  Google Scholar 

• Ni, L., & Guzdial, M. (2012). Who am I? Understanding high school computer science teachers’ professional identity. In Proceedings of the 43rd ACM technical symposium on computer science education (pp. 499–504). Raleigh, NC.

  Google Scholar 

• Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.

  Google Scholar 

• Papert, S. (1991). Situating constructionism. In I. Harel & S. Papert (Eds.), Constructionism (pp. 1–11). Norwood: Ablex.

  Google Scholar 

• Penner, D. E. (2000). Cognition, computers, and synthetic science: Building knowledge and meaning through modeling. Review of Research in Education, 25, 1–36.

  Google Scholar 

• Perković, L., Settle, A., Hwang, S., & Jones, J. (2010). A framework for computational thinking across the curriculum. In Proceedings of the fifteenth annual conference on Innovation and technology in computer science education (pp. 123–127). ACM.

  Google Scholar 

• Repenning, A., Webb, D., & Ioannidou, A. (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) (pp. 265–269). New York: ACM Press.

  Google Scholar 

• Repenning, A., Webb, D. C., Koh, K. H., Nickerson, H., Miller, S., Brand, C., Horses, I. H. M., Basawapatna, A., Gluck, F., Grover, R., Gutierrez, K., & Repenning, N. (2014). Scalable game design: A strategy to bring systemic computer science education to schools through game design and simulation creation. ACM Transactions on Computing Education (TOCE), 15(2), 11. https://doi.org/10.1145/2700517.

  Article  Google Scholar 

• Roschelle, J., Kaput, J., & Stroup, W. (2000) SimCalc: Accelerating student engagement with the mathematics of change. In Learning the sciences of the 21st century: Research, design, and implementing advanced technology learning environments (pp 47–75).

  Google Scholar 

• Rubinstein, A., & Chor, B. (2014). Computational thinking in life science education. PLoS Computational Biology, 10(11). https://doi.org/10.1371/journal.pcbi.1003897.

• Ryoo, J., Goode, J., & Margolis, J. (2016). It takes a village: Supporting inquiry- and equity-oriented computer science pedagogy through a professional learning community. Computer Science Education. https://doi.org/10.1080/08993408.2015.1130952.

• Schanzer, E., Fisler, K., Krishnamurthi, S., & Felleisen, M. (2015). 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). New York: ACM.

  Google Scholar 

• Selby, C. C. (2015). Relationships: Computational thinking, pedagogy of programming, and Bloom’s Taxonomy. In Proceedings of the Workshop in primary and secondary computing education (pp. 80–87). New York: ACM.

  Google Scholar 

• Sengupta, P., & Farris, A. V. (2013). Learning kinematics in elementary grades using agent-based computational Modeling: A visual programming based approach. In Proceedings of the 11th International conference on interaction design & children (pp 78–87).

  Google Scholar 

• Sengupta, P., & Wilensky, U. (2009). Learning electricity with NIELS: Thinking with electrons and thinking in levels. International Journal of Computers for Mathematical Learning, 14(1), 21–50.

  Article  Google Scholar 

• Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380.

  Article  Google Scholar 

• Sengupta, P., Krishnan, G., Wright, M., & Ghassoul, C. (2015). Mathematical machines and integrated stem: An Intersubjective constructionist approach. In S. Zvacek, M. Restivo, J. Uhomoibhi, & M. Helfert (Eds.), Computer supported education, Communications in Computer and Information Science (Vol. 510, pp. 272–288). Cham, Switzerland: Springer.

  Chapter  Google Scholar 

• Sherin, B. L. (2001). A comparison of programming languages and algebraic notation as expressive languages for physics. International Journal of Computers for Mathematics Learning, 6(1), 1–61.

  Article  Google Scholar 

• Smith, M. (2016). Computer science for all. Washington, DC: Office of Science and Technology Policy, Executive Office of the President Retrieved from https://www.whitehouse.gov/blog/2016/01/30/computer-scienceall

• Simard, C., Stephenson, C., & Kosaraju, D. (2010). Addressing Core Equity Issues in K–12 Computer Science Education: Identifying barrier and sharing strategies. Palo Alto, CA: The Anita Borg Institute and the Computer Science Teachers Association.

  Google Scholar 

• Tan, J., & Biswas, G. (2007). Simulation-based game learning environments: Building and sustaining a fish tank. In Proceedings of the First IEEE International Workshop on Digital game and intelligent toy enhanced learning (pp. 73–80). Jhongli, Taiwan.

  Google Scholar 

• Taub, R., Armoni, M., Bagno, E., & Ben-Ari, M. (2015). The effect of computer science on physics learning in a computational science environment. Computer Education, 87, 10–23.

  Article  Google Scholar 

• Teodte, R., & Aydeniz, M. (2015). Computational thinking and impacts on K-12 science education. Published in Proceedings of the 2015 IEEE Frontiers in Education Conference (FIE), DOI: https://doi.org/10.1109/FIE.2015.7344239

• The National Center for Women & Information Technology (NCWIT). (2012). NSF PI Meeting. http://www.ncwit.org/sites/default/files/legacy/pdf/Pre-Service%20Curriculum%20-%20Goode.pdf

• The Royal Society. (2012). Shut down or restart: The way forward for computing in UK schools. Retrieved from http://royalsociety.org/education/policy/computing-in-schools/report/

• The UK Department for Education. (2013). National curriculum in England: Computing programmes of study. Available [Online] https://www.gov.uk/government/publications/national-curriculum-in-england-computing-programmes-of-study/national-curriculum-in-england-computing-programmes-of-study

• The White House. (2016). Computer science for all. Retrieved from https://www.whitehouse.gov/blog/2016/01/30/computer-science-all

• Vee, A. (2013). Understanding computer programming as a literacy. Literacy in Composition Studies, 1(2), 42–64.

  Article  Google Scholar 

• 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://link.springer.com/article/10.1007%2Fs10956-015-9581-5

• Wilensky, U. (1995). Learning probability through building computational models. Proceedings of the 19th International Conference on the Psychology of Mathematics Education. Recife, Brazil, July 1995.

  Google Scholar 

• Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep or a firefly: Learning biology through constructing and testing computational theories—An embodied modeling approach. Cognition & Instruction, 24(2), 171–209.

  Article  Google Scholar 

• Wilensky, U., Brady, C., & Horn, M. (2014). Fostering computational literacy in science classrooms. Communications of the ACM, 57(8), 17–21.

  Article  Google Scholar 

• Wilkerson-Jerde, M. H. (2014). Construction, categorization, and consensus: Student generated computational artifacts as a context for disciplinary reflection. Educational Technology Research and Development, 62(1), 99–121.

  Article  Google Scholar 

• Wilson, C., Sudol, L. A., Stephenson, C., & Stehlik, M. (2010). Running on empty: The failure to teach K-12 computer science in the digital age. New York: The Association for Computing Machinery and the Computer Science Teachers Association.

  Google Scholar 

• Wing, J. (2010). Computational thinking: What and why? Unpublished manuscript in progress, Available [Online] http://www.cs.cmu.edu/~CompThink/resources/TheLinkWing.pdf

• World Bank. (2016). World Development Report 2016: Digital dividends. Washington, DC: World Bank. doi:10.1596/978-1-4648-0671-1. License: Creative commons attribution CC BY 3.0 IGO http://documents.worldbank.org/curated/en/896971468194972881/pdf/102725-PUB-Replacement-PUBLIC.pdf

• Yadav, A., & Korb, J. T. (2012). Learning to teach computer science: The need for a methods course. Communications of the Association for Computing Machinery, 55, 31–33.

  Article  Google Scholar 

• Yadav, A., Gretter, S., Hambrusch, S. S., & Sands, P. (2017). Expanding computer science education in schools: understanding teacher experiences and challenges. Computer Science Education, 26(4), 235–254. https://doi.org/10.1080/08993408.2016.1257418.

• Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 1–16.

  Google Scholar 

Download references