# Comparing Virtual and Physical Robotics Environments for Supporting Complex Systems and Computational Thinking

- 1.8k Downloads
- 15 Citations

## Abstract

Both complex systems methods (such as agent-based modeling) and computational methods (such as programming) provide powerful ways for students to understand new phenomena. To understand how to effectively teach complex systems and computational content to younger students, we conducted a study in four urban middle school classrooms comparing 2-week-long curricular units—one using a physical robotics participatory simulation and one using a virtual robotics participatory simulation. We compare the two units for their effectiveness in supporting students’ complex systems thinking and computational thinking skills. We find that while both units improved student outcomes to roughly the same extent, they engendered different *perspectives* on the content. That is, students using the physical system were more likely to interpret situations from a bottom-up (“agent”) perspective, and students using the virtual system were more likely to employ a top-down (“aggregate”) perspective. Our outcomes suggest that the medium of students’ interactions with systems leads to differences in their learning from and about those systems. We explore the reasons for and effects of these differences, challenges in teaching this content, and student learning gains. The paper contributes operationalizable definitions of *complex systems perspectives* and *computational perspectives* and provides both a theoretical framework for and empirical evidence of a relationship between those two perspectives.

## Keywords

Computational thinking Systems thinking Robotics Participatory simulations## References

- Azhar MQ, Goldman R, Sklar E (2006) An agent-oriented behavior-based interface framework for educational robotics. In: Proceedings of the conference on autonomous agents and multiagent systems (AAMAS 2006)Google Scholar
- Basawapatna A, Koh KH, Repenning A, Webb DC, Marshall KS (2011) Recognizing computational thinking patterns. In: Proceedings of the 42nd ACM technical symposium on computer science education. SIGCSE 2011, pp 245–250Google Scholar
- Ben-Ari M (2001) Constructivism in computer science education. J Comput Math Sci Teach 20(1):45–73Google Scholar
- Berland M (2008) VBOT: Motivating complex systems and computational literacies in virtual and physical robotics learning environments. Retrieved from ProQuest Digital Dissertations. AAT 3307005Google Scholar
- Berland M, Wilensky U (2005) Complex play systems—results from a classroom implementation of VBOT. In: The annual meeting of the American Educational Research Association, Montreal, Canada, April 11–15, 2005Google Scholar
- Berland M, Wilensky U (2008) VBOT (computer software)Google Scholar
- Berland M, Martin T, Benton T, Petrick C (2011) Programming on the move: design lessons from IPRO. In: Proceedings of ACM SIG-CHI 2011, pp 2149–2154Google Scholar
- Berland M, Martin T, Benton T, Smith CP, Davis D (2013) Using learning analytics to understand the learning pathways of novice programmers. J Learn Sci 22(4):564–599. doi: 10.1080/10508406.2013.836655 CrossRefGoogle 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. Int J Comput Math Learn 14(2):81–119CrossRefGoogle Scholar
- Boehm BW, Brown JR, Lipow M (1976) Quantitative evaluation of software quality. In: Proceedings of the 2nd international conference of software engineering, Los Alamitos, CAGoogle Scholar
- Braitenberg V (1984) Vehicles: Experiments in synthetic psychology. MIT Press, Cambridge, MAGoogle Scholar
- Bundy A (2007) Computational thinking is pervasive. J Sci Pract Comput 1(2):67–69Google Scholar
- Chi M (2005) Commonsense conceptions of emergent processes: why some misconceptions are robust. J Learn Sci 14(2):161–199CrossRefGoogle Scholar
- Cobb P, Confrey J, diSessa A, Lehrer R (2003) Design experiments in educational research. Educ Res 32(1):9–13CrossRefGoogle Scholar
- Colella V (2000) Participatory simulations: building collaborative understanding through immersive dynamic modeling. J Learn Sci 9(4):471–500CrossRefGoogle Scholar
- Collier N (2003) Repast: an extensible framework for agent simulation. The University of Chicago’s Social Science Research, p 36Google Scholar
- Collins A, Joseph D, Bielaczyc K (2004) Design research: theoretical and methodological issues. J Learn Sci 13(1):15–42CrossRefGoogle Scholar
- Davis B, Sumara D (2006) Complexity and education: inquiries into learning, teaching, and research. Lawrence Erlbaum, Mahwah, NJGoogle Scholar
- diSessa A (2001) Changing minds: computers, learning, and literacy. MIT Press, Cambridge, MAGoogle Scholar
- diSessa A, Cobb P (2004) Ontological innovation and the role of theory in design experiments. J Learn Sci 13(1):77–103CrossRefGoogle Scholar
- Druin A, Hendler JA (2000) Robots for kids: exploring new technologies for learning. Morgan Kaufmann, BurlingtonGoogle Scholar
- Goldstone RL, Wilensky U (2008) Promoting transfer by grounding complex systems principles. J Learn Sci 17(4):465–516CrossRefGoogle Scholar
- Grotzer TA, Basca BB (2003) How does grasping the underlying causal structures of ecosystems impact students’ understanding? J Biol Educ 38(1):16–29CrossRefGoogle Scholar
- Guzdial M, Forte A (2005) Design process for a non-majors computing course. ACM SIGCSE Bulletin 37(1):361–365CrossRefGoogle Scholar
- Hancock C (2003) Real-time programming and the big ideas of computational literacy. Unpublished doctoral dissertation, MIT, Cambridge, MAGoogle Scholar
- Harel I, Papert S (1990) Software design as a learning environment. Interact Learn Environ 1(1):1–32CrossRefGoogle Scholar
- Hmelo CE, Holton DL, Kolodner JL (2000) Designing to learn about complex systems. J Learn Sci 9(3):247–298CrossRefGoogle Scholar
- Hmelo-Silver C, Pfeffer MG (2004) Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cogn Sci 28(1):127–138CrossRefGoogle Scholar
- Holland JH (1995) Hidden order: how adaptation builds complexity. Basic BooksGoogle Scholar
- Holland J (1999) Emergence: from chaos to order. Basic Books, New York, NYGoogle Scholar
- Ioannidou A, Repenning A, Lewis C, Cherry G, Rader C (2003) Making constructionism work in the classroom. Int J Comput Math Learn 8(1):63–108CrossRefGoogle Scholar
- Ishii H (2008) Tangible bits: beyond pixels. In: Proceedings of the 2nd international ACM conference on tangible and embedded interaction, pp xv–xxvGoogle Scholar
- Jacobson M, Wilensky U (2006) Complex systems in education: scientific and educational importance and implications for the learning sciences. J Learn Sci 15(1):11–34CrossRefGoogle Scholar
- Johnson S (2002) Emergence: the connected lives of ants, brains, cities, and software. Scribner, New York, NYGoogle Scholar
- Kelleher C, Pausch R, Kiesler S (2007) Storytelling ALICE motivates middle school girls to learn computer programming. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp 1455–1464. San Jose, CA, April 28–May 3, 2007Google Scholar
- Klopfer E, Colella V, Resnick M (2002) New paths on a StarLogo adventure. Comput Graph 26(4):615–622CrossRefGoogle Scholar
- Klopfer E, Yoon S, Rivas L (2004) Comparative analysis of palm and wearable computers for participatory simulations. J Comput Assist Learn 20(5):347–359CrossRefGoogle Scholar
- Klopfer E, Yoon S, Um T (2005) Young adventurers—modeling of complex dynamic systems with elementary and middle-school students. J Comput Math Sci Teach 24(2):157–178Google Scholar
- Lahtinen E, Ala-Mutka K, Järvinen HM (2005) A study of the difficulties of novice programmers. ACM SIGCSE Bull 37(3):14–18CrossRefGoogle Scholar
- Levy ST, Wilensky U (2008) Inventing a “mid level” to make ends meet: reasoning between the levels of complexity. Cogn Instruct 26(1):1–47CrossRefGoogle Scholar
- Luke S, Cioffi-Revilla C, Panait L, Sullivan K, Balan G (2005) MASON: a multiagent simulation environment. Simulation 81(7):517CrossRefGoogle Scholar
- Maes P (1990) Designing autonomous agents: theory and practice from biology to engineering and back. MIT Press, Cambridge, MAGoogle Scholar
- Martin FG (1996) Ideal and real systems: a study of notions of control in undergraduates who design robots. In: Kafai Y, Resnick M (eds) Constructionism in practice: rethinking the roles of technology in learning. MIT Press, Cambridge, MAGoogle Scholar
- Martin T, Berland M, Benton T, Smith CP (2013) Learning programming with IPRO: the effects of a mobile, social programming environment. J Interact Learn Res 24(3):301–328Google Scholar
- National Research Council (2010) Report of a workshop on the scope and nature of computational thinking. National Academies Press, Washington, DCGoogle Scholar
- Papert S (1975) Teaching children thinking. J Struct Lang 4:219–229Google Scholar
- Papert S (1980) Mindstorms: children, computers, and powerful ideas. Basic Books, New York, NYGoogle Scholar
- Parker LE, Schultz A (eds) (2005) Multi-robot systems: from swarms to intelligent automata, vol III. Kluwer, NetherlandsGoogle Scholar
- Pea RD (1987) Cognitive technologies for mathematics education. In: Schoenfeld A (ed) Cognitive science and mathematics education. Lawrence Erlbaum Associates Inc, Hillsdale, NJ, pp 89–122Google Scholar
- Pea RD, Kurland DM (1984) On the cognitive effects of learning computer programming. New Ideas Psychol 2(2):137–168CrossRefGoogle Scholar
- Penner DE (2000) Explaining systems: investigating middle school students’ understanding of emergent phenomena. J Res Sci Teach 37(8):784–806CrossRefGoogle Scholar
- Perkins DN, Grotzer TA (2005) Dimensions of causal understanding: the role of complex causal models in students’ understanding of science. Stud Sci Edu 41(1):117–166CrossRefGoogle Scholar
- Portsmore M (2005) ROBOLAB: intuitive robotic programming software to support lifelong learning. Apple learning technology review. Spring/Summer, 2005Google Scholar
- Resnick M (2003) Thinking like a tree (and other forms of ecological thinking). Int J Comput Math Learn 8(1):43–62CrossRefGoogle Scholar
- Resnick M, Ocko S, Papert S (1988) LEGO, logo, and design. Child Environ Q 5(4):14–18Google Scholar
- Resnick M, Wilensky U (1998) Diving into complexity: developing probabilistic decentralized thinking through role-playing activities. J Learn Sci 7(2):153–172CrossRefGoogle Scholar
- Schoenfeld AH (1992) Learning to think mathematically: problem solving, metacognition, and sense making in mathematics. Handbook of research on mathematics teaching and learning, pp 334–370Google Scholar
- Schunk DH (1983) Ability versus effort attributional feedback: differential effects on self-efficacy and achievement. J Educ Psychol 75(6):848CrossRefGoogle Scholar
- Schweikardt E, Gross MD (2006) roBlocks: a robotic construction kit for mathematics and science education. Proceedings of the 8th international conference on Multimodal interfaces, pp 72–75Google Scholar
- Sengupta P, Wilensky U (2009) Learning electricity with NIELS: thinking with electrons and thinking in levels. Int J Comput Math Learn 14(1):21–50CrossRefGoogle Scholar
- Sharlin E, Watson BA, Kitamura Y, Kishino F, Itoh Y (2004) On humans, spatiality and tangible user interfaces. Pervasive Ubiquitous Comput 8(5), 338–346. Theme issue on tangible interfaces in perspectiveGoogle Scholar
- Sipitakiat A, Blikstein P (2010) Think globally, build locally: a technological platform for low-cost, open-source, locally-assembled programmable bricks for education. In: Presented at the conference on tangible, embedded, and embodied interaction TEI 2010, Cambridge, USAGoogle Scholar
- Sklar E, Eguchi A, Johnson J (2003a) RoboCupJunior: learning with educational robotics. RoboCup 2002: robot soccer world cup VI, pp 238–253Google Scholar
- Sklar E, Parsons S, Stone P (2003b) Robocup in higher education: a preliminary report. In: Proceedings of the 7th RoboCup symposiumGoogle Scholar
- Soloway E (1986) Learning to program = learning to construct mechanisms and explanations. Commun ACM 29(9):850–858CrossRefGoogle Scholar
- Wilensky U (1999) NetLogo [Computer software]. Evanston, IL: Northwestern University, Center for Connected Learning and Computer-Based Modeling. Retrieved September 20, 2011, from http://ccl.northwestern.edu/netlogo
- Wilensky U (2003) Statistical mechanics for secondary school: the GasLab modeling toolkit. Int J Comput Math Learn 8(1):1–4CrossRefGoogle 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. Cogn Instruct 24(2):171–209CrossRefGoogle Scholar
- Wilensky U, Resnick M (1999) Thinking in levels: a dynamic systems perspective to making sense of the world. J Sci Educ Technol 8(1):3–19CrossRefGoogle Scholar
- Wilensky U, Stroup W (1999a) Learning through participatory simulations: network-based design for systems learning in classrooms. In: Proceedings of the 1999 conference on computer support for collaborative learning, CSCL ‘99 Palo Alto, CAGoogle Scholar
- Wilensky U, Stroup W (1999b) HubNet [Computer software]. Northwestern University, Center for Connected Learning and Computer-Based Modeling, Evanston, ILGoogle Scholar
- Wing JM (2006) Computational thinking. Commun ACM 49(3):33–35CrossRefGoogle Scholar
- Wolfram S (2002) A new kind of science. Wolfram Media, Champaign, ILGoogle Scholar
- Wyeth P (2008) How young children learn to program with sensor, action, and logic blocks. J Learn Sci 17(4):517–550CrossRefGoogle Scholar