Eben B. Witherspoon
Skip Abstract Section
Abstract
Computational thinking describes key principles from computer science that are broadly generalizable. Robotics programs can be engaging learning environments for acquiring core computational thinking competencies. However, few empirical studies evaluate the effectiveness of a robotics programming curriculum for developing computational thinking knowledge and skills. This study measures pre/post gains with new computational thinking assessments given to middle school students who participated in a virtual robotics programming curriculum. Overall, participation in the virtual robotics curriculum was related to significant gains in pre- to posttest scores, with larger gains for students who made further progress through the curriculum. The success of this intervention suggests that participation in a scaffolded programming curriculum, within the context of virtual robotics, supports the development of generalizable computational thinking knowledge and skills that are associated with increased problem-solving performance on nonrobotics computing tasks. Furthermore, the particular units that students engage in may determine their level of growth in these competencies.
- ACM Education Policy Committee. 2014. Rebooting the Pathway to Success: Preparing Students for Computing Workforce Needs in the United States. Retrieved from http://pathways.acm.org/ACM_pathways_report.pdf.Google Scholar
- D. Alimisis and C. Kynigos. 2009. Constructionism and robotics in education. In D. Alimisis (Ed.), Teacher Education on Robotics-Enhanced Constructivist Pedagogical Methods. School of Pedagogical and Tech. Education, Athens, Greece, 11--26.Google Scholar
- D. Alimisis. 2012. Robotics in education 8 education in robotics: Shifting focus from technology to pedagogy. In Proceedings of the International Conference on Robotics in Education. Matfyz Press, 7--14.Google Scholar
- R. D. Atkinson and M. Mayo. 2011. Refueling the U.S. Innovation Economy: Fresh Approaches to Science, Technology, Engineering and Mathematics (STEM) Education. The Information Technology and Innovation Foundation. Retrieved from http://www.itif.org/files/2010-refueling-innovation-economy.pdf.Google Scholar
- V. Barr and C. Stephenson. 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. DOI:10.1145/1929887.1929905 Google ScholarDigital Library
- F. B. V. Benitti. 2012. Exploring the educational potential of robotics in schools: A systematic review. Computers and Education 58, 3, 978--988. DOI:10.1016/j.compedu.2011.10.006 Google ScholarDigital Library
- K. Brennan and M. Resnick. 2012. New frameworks for studying and assessing the development of computational thinking. In Proceedings of the American Educational Research Association. 1--25. Retrieved from http://scratched.gse.harvard.edu/ct/files/AERA2012.pdf.Google Scholar
- R. Catrambone. 1998. The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General 127, 4, 355--376. Google ScholarCross Ref
- D. H. Clements and D. F. Gullo. 1984. Effects of computer programming on young children's cognition. Journal of Educational Psychology 76, 6, 1051--1058. DOI:10.1037/0022-0663.76.6.1051 Google ScholarCross Ref
- J. Cohen. 1992. A power primer. Psychological Bulletin 112, 1, 155--159. DOI:10.1037/0033-2909.112.1.155 Google ScholarCross Ref
- Y. Doppelt, C. D. Schunn, E. M. Silk, M. M. Mehalik, B. Reynolds, and E. Ward. 2009. Evaluating the impact of a facilitated learning community approach to professional development on teacher practice and student achievement. Research in Science 8 Technological Education 27, 3, 339--354. DOI:10.1080/02635140903166026Google Scholar
- Educational Testing Service. 2015. The Praxis Study Companion: Technology Education. Educational Testing Service, Princeton, NJ. Retrieved from https://www.ets.org/s/praxis/pdf/5051.pdf.Google Scholar
- A. Eguchi. 2014. Learning experience through robocupjunior: Promoting engineering and computational thinking through robotics competition. In Proceedings of the American Society for Engineering Education. Retrieved from https://peer.asee.org/20743.Google Scholar
- B. Ericson and M. Guzdial. 2014. Measuring demographics and performance in computer science education at a nationwide scale using AP CS data. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (SIGCSE’14). ACM Press, New York, 217--222. DOI:10.1145/2538862.2538918 Google ScholarDigital Library
- J. Goode and J. Margolis. 2011. Exploring computer science. ACM Transactions on Computing Education 11, 2, 1--16. DOI:10.1145/1993069.1993076 Google ScholarDigital Library
- S. Grover and R. Pea. 2013. Computational thinking in K-12: A review of the state of the field. Educational Researcher 42, 1, 38--43. DOI:10.3102/0013189X12463051 Google ScholarCross Ref
- M. Guzdial, B. Ericson, T. Mcklin, and S. Engelman. 2014. Georgia computes! An intervention in a US state, with formal and informal education in a policy context. ACM Transactions on Computing Education (TOCE) 14, 2, 13.Google Scholar
- S. Hambrusch, C. Hoffmann, J. T. Korb, M. Haugan, and A. L. Hosking. 2009. A multidisciplinary approach towards computational thinking for science majors. ACM SIGCSE Bulletin 41, 183. DOI:10.1145/1539024.1508931 Google ScholarDigital Library
- J. B. Harris and M. J. Hofer. 2011. Technological pedagogical content knowledge (TPACK) in action: A descriptive study of secondary teachers’ curriculum-based, technology-related instructional planning. Journal of Research on Technology in Education 43, 3, 211--229. Google ScholarCross Ref
- C. Kelleher and R. Pausch. 2005. Lowering the barriers to programming. ACM Computing Surveys 37, 2, 83--137. DOI:10.1145/1089733.1089734 Google ScholarDigital Library
- G. Khoury. 2007. CSTA Certification Committee Report: Computer Science State Certification Requirements. Computer Science Teachers Association, New York. Retrieved from http://www.csta.acm.org/ComputerScienceTeacherCertifi cation/sub/TeachCertRept07New.pdf.Google Scholar
- C. Kim, M. K. Kim, C. Lee, J. M. Spector, and K. DeMeester. 2013. Teacher beliefs and technology integration. Teaching and Teacher Education 29, 1, 76--85. DOI:10.1016/j.tate.2012.08.005 Google ScholarCross Ref
- D. Klahr and S. M. Carver. 1988. Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology 20, 3, 362--404. DOI:10.1016/0010-0285(88)90004-7 Google ScholarCross Ref
- K. H. Koh, A. Repenning, H. Nickerson, Y. Endo, and P. Motter. 2013. Will it stick? Exploring the sustainability of computational thinking education through game design. In Proceedings of the 44th ACM Technical Symposium on Computer Science Education (SIGCSE’13). ACM Press, New York, 597--602. DOI:10.1145/2445196.2445372 Google ScholarDigital Library
- R. Kurland, C. Pea, and Clement R. Mawby. 1986. A study of the development of programming ability and thinking skills in high school students. Journal of Educational Computing Research 2, 4, 429--458Google ScholarCross Ref
- J. Lave and E. Wenger. 1991. Situated Learning: Legitimate Peripheral Participation. Cambridge University Press, Cambridge, UK. Google ScholarCross Ref
- I. Lee, F. Martin, and K. Apone. 2014. Integrating computational thinking across the K--8 curriculum. ACM Inroads 5, 4, 64--71. DOI:10.1145/2684721.2684736 Google ScholarDigital Library
- A. S. Liu, J. Newsom, C. Schunn, and R. Shoop. 2013. Students learn programming faster through robotic simulation. Tech Directions 72, 16--19. Retrieved from http://www.education.rec.ri.cmu.edu/content/educators/research/files/p16-19 Shoop et al.pdf.Google Scholar
- A. S. Liu, C. D. Schunn, J. Flot, and R. Shoop. 2013. The role of physicality in rich programming environments. Computer Science Education 23, 4, 315--331. DOI:10.1080/08993408.2013.847165 Google ScholarCross Ref
- S. Y. Lye and J. H. L. Koh. 2014. Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior 41, 51--61. DOI:10.1016/j.chb.2014.09.012 Google ScholarDigital Library
- L. Major, T. Kyriacou, and O. P. Brereton. 2012. Systematic literature review: Teaching novices programming using robots. IET Software 6, 6, 502. DOI:10.1049/iet-sen.2011.0125Google ScholarCross Ref
- R. E. Mayer. 1974. Acquisition processes and resilience under varying testing conditions for structurally different problem-solving procedures. Journal of Educational Psychology 66, 5, 644--656. DOI:10.1037/h0037482 Google ScholarCross Ref
- R. E. Mayer. 2008. Applying the science of learning: evidence-based principles for the design of multimedia instruction. American Psychologist 63, 8, 760--769. DOI:10.1037/0003-066X.63.8.760 Google ScholarCross Ref
- R. E. Mayer and J. G. Greeno. 1972. Structural differences between outcomes produced by different instructional methods. Journal of Educational Psychology 63, 12, 165--173. DOI:10.1037/h0032654 Google ScholarCross Ref
- A. Melchior, F. Cohen, T. Cutter, and T. Leavitt. 2005. More Than Robots: An Evaluation of the FIRST Robotics Competition Participant and Institutional Impacts. Brandeis University Heller School for Social Policy and Management, Waltham, MA.Google Scholar
- National Research Council, Committee for the Workshops on Computational Thinking. 2010. The Scope and Nature of Computational Thinking. National Research Council, Washington, DC. Retrieved from http://www.nap.edu/catalog/12840.html.Google Scholar
- National Science Board. 2015. Revisiting the STEM Workforce, A Companion to Science and Engineering Indicators 2014. National Science Board, Arlington, VA. Retrieved from http://www.nsf.gov/nsb/publications/2015/nsb201510.pdf.Google Scholar
- S. Y. Okita. 2013. The relative merits of transparency: Investigating situations that support the use of robotics in developing student learning adaptability across virtual and physical computing platforms. British Journal of Educational Technology 45, 5, 844--862. DOI:10.1111/bjet.12101 Google ScholarCross Ref
- D. B. Palumbo. 1990. Programming language/problem-solving research: A review of relevant issues. Review of Educational Research 60, 1, 65--89. DOI:10.3102/00346543060001065 Google ScholarCross Ref
- R. Pea. 1983. LOGO Programming and problem solving. In Proceedings of the American Educational Research Association (AERA’83). Retrieved from https://web.stanford.edu/∼roypea/RoyPDF%20folder/A39_Pea_87d_CCT_TR_MS.pdf.Google Scholar
- S. Puntambekar and R. Hubscher. 2005. Tools for scaffolding students in a complex learning environment: What have we gained and what have we missed? Educational Psychologist 40, 1, 1--12. Retrieved from http://doi.org/10.1207/s15326985ep4001.Google ScholarCross Ref
- A. Repenning, D. Webb, and A. 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, 265--269. DOI:10.1145/1734263.1734357 Google ScholarDigital Library
- A. Robins, J. Rountree, and N. Rountree. 2003. Learning and teaching programming: A review and discussion. Computer Science Education 13, 2, 137--172. DOI:10.1076/csed.13.2.137.14200 Google ScholarCross Ref
- G. Salomon and D. N. Perkins. 1987. Transfer of cognitive skills from programming: When and how? Journal of Educational Computing Research 3, 149--170. Google ScholarCross Ref
- L. S. Shulman. 1986. Those who understand: knowledge growth in teaching. Educational Researcher 15, 2, 4--14. DOI:10.3102/0013189X015002004 Google ScholarCross Ref
- E. Silk, C. Schunn, and R. Shoop. 2009. Synchronized robot dancing: Motivating efficiency 8 meaning in problem solving with robotics. Robot Magazine 17, 42--45.Google Scholar
- L. Wang, P. A. Ertmer, and T. J. Newby. 2004. Increasing preservice teachers’ self-efficacy beliefs for technology integration. Journal of Research on Technology in Education 36, 3, 231--250. DOI:10.1080/15391523.2004.10782414 Google ScholarCross Ref
- D. Weintrop, E. Beheshti, M. Horn, K. Orton, K. Jona, L. Trouille, and U. Wilensky. 2016. Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology 25, 1, 127--147. DOI:10.1007/s10956-015-9581-5 Google ScholarCross Ref
- L. Werner, J. Denner, S. Campe, and D. C. Kawamoto. 2012. The fairy performance assessment: Measuring computational thinking in middle school. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (SIGCSE’12). ACM, New York, 215--220. DOI:10.1145/2157136.2157200 Google ScholarDigital Library
- J. M. Wing. 2006. Computational thinking. Communications of the ACM 49, 3, 33. DOI:10.1145/1118178.1118215Google ScholarDigital Library
- L. K. Zech, C. L. Gause-Vega, M. H. Bray, T. Secules, and S. R. Goldman. 2000. Content-based collaborative inquiry: A professional development model for sustaining educational reform. Educational Psychologist 35, 3, 207--217. DOI:10.1207/S15326985EP3503_6 Google ScholarCross Ref
Index Terms
- Developing Computational Thinking through a Virtual Robotics Programming Curriculum
Recommendations
A model for piloting pathways for computational thinking in a general education curriculum
SIGCSE '11: Proceedings of the 42nd ACM technical symposium on Computer science educationComputational thinking has been identified as a necessary fundamental skill for all students. University curricula, however, are currently not designed to provide such knowledge to a broad student population. In this paper, we report on our experiences ...
Computational Thinking for All: An Experience Report on Scaling up Teaching Computational Thinking to All Students in a Major City in Sweden
SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science EducationThe Swedish government has recently introduced digital competence including programming in the Swedish K-9 curriculum starting no later than fall 2018. This means that 100 000 teachers need to learn programming and digital competence in less than a ...
Evaluation and development of STEAM teachers’ computational thinking skills: Analysis of multiple influential factors
AbstractComputational thinking (CT) has become the basic foothold of STEAM education. The role of teachers as an essential element of CT education cannot be ignored. Therefore, measuring teachers’ ability to integrate CT into classroom is necessary. There ...
Comments