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

Springer Science and Business Media LLC - Tập 24 Số 5 - Trang 628-647 - 2015
Matthew Berland1, Uri Wilensky2
1Department of Curriculum and Instruction, University of Wisconsin–Madison, Madison, USA
2Departments of Learning Sciences and Computer Science, Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Chicago, USA

Tóm tắt

Từ khóa


Tài liệu tham khảo

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)

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 245–250

Ben-Ari M (2001) Constructivism in computer science education. J Comput Math Sci Teach 20(1):45–73

Berland M (2008) VBOT: Motivating complex systems and computational literacies in virtual and physical robotics learning environments. Retrieved from ProQuest Digital Dissertations. AAT 3307005

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, 2005

Berland M, Wilensky U (2008) VBOT (computer software)

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 2149–2154

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

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):81–119

Boehm BW, Brown JR, Lipow M (1976) Quantitative evaluation of software quality. In: Proceedings of the 2nd international conference of software engineering, Los Alamitos, CA

Braitenberg V (1984) Vehicles: Experiments in synthetic psychology. MIT Press, Cambridge, MA

Bundy A (2007) Computational thinking is pervasive. J Sci Pract Comput 1(2):67–69

Chi M (2005) Commonsense conceptions of emergent processes: why some misconceptions are robust. J Learn Sci 14(2):161–199

Cobb P, Confrey J, diSessa A, Lehrer R (2003) Design experiments in educational research. Educ Res 32(1):9–13

Colella V (2000) Participatory simulations: building collaborative understanding through immersive dynamic modeling. J Learn Sci 9(4):471–500

Collier N (2003) Repast: an extensible framework for agent simulation. The University of Chicago’s Social Science Research, p 36

Collins A, Joseph D, Bielaczyc K (2004) Design research: theoretical and methodological issues. J Learn Sci 13(1):15–42

Davis B, Sumara D (2006) Complexity and education: inquiries into learning, teaching, and research. Lawrence Erlbaum, Mahwah, NJ

diSessa A (2001) Changing minds: computers, learning, and literacy. MIT Press, Cambridge, MA

diSessa A, Cobb P (2004) Ontological innovation and the role of theory in design experiments. J Learn Sci 13(1):77–103

Druin A, Hendler JA (2000) Robots for kids: exploring new technologies for learning. Morgan Kaufmann, Burlington

Goldstone RL, Wilensky U (2008) Promoting transfer by grounding complex systems principles. J Learn Sci 17(4):465–516

Grotzer TA, Basca BB (2003) How does grasping the underlying causal structures of ecosystems impact students’ understanding? J Biol Educ 38(1):16–29

Guzdial M, Forte A (2005) Design process for a non-majors computing course. ACM SIGCSE Bulletin 37(1):361–365

Hancock C (2003) Real-time programming and the big ideas of computational literacy. Unpublished doctoral dissertation, MIT, Cambridge, MA

Harel I, Papert S (1990) Software design as a learning environment. Interact Learn Environ 1(1):1–32

Hmelo CE, Holton DL, Kolodner JL (2000) Designing to learn about complex systems. J Learn Sci 9(3):247–298

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):127–138

Holland JH (1995) Hidden order: how adaptation builds complexity. Basic Books

Holland J (1999) Emergence: from chaos to order. Basic Books, New York, NY

Ioannidou A, Repenning A, Lewis C, Cherry G, Rader C (2003) Making constructionism work in the classroom. Int J Comput Math Learn 8(1):63–108

Ishii H (2008) Tangible bits: beyond pixels. In: Proceedings of the 2nd international ACM conference on tangible and embedded interaction, pp xv–xxv

Jacobson M, Wilensky U (2006) Complex systems in education: scientific and educational importance and implications for the learning sciences. J Learn Sci 15(1):11–34

Johnson S (2002) Emergence: the connected lives of ants, brains, cities, and software. Scribner, New York, NY

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, 2007

Klopfer E, Colella V, Resnick M (2002) New paths on a StarLogo adventure. Comput Graph 26(4):615–622

Klopfer E, Yoon S, Rivas L (2004) Comparative analysis of palm and wearable computers for participatory simulations. J Comput Assist Learn 20(5):347–359

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):157–178

Lahtinen E, Ala-Mutka K, Järvinen HM (2005) A study of the difficulties of novice programmers. ACM SIGCSE Bull 37(3):14–18

Levy ST, Wilensky U (2008) Inventing a “mid level” to make ends meet: reasoning between the levels of complexity. Cogn Instruct 26(1):1–47

Luke S, Cioffi-Revilla C, Panait L, Sullivan K, Balan G (2005) MASON: a multiagent simulation environment. Simulation 81(7):517

Maes P (1990) Designing autonomous agents: theory and practice from biology to engineering and back. MIT Press, Cambridge, MA

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, MA

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):301–328

National Research Council (2010) Report of a workshop on the scope and nature of computational thinking. National Academies Press, Washington, DC

Papert S (1975) Teaching children thinking. J Struct Lang 4:219–229

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

Parker LE, Schultz A (eds) (2005) Multi-robot systems: from swarms to intelligent automata, vol III. Kluwer, Netherlands

Pea RD (1987) Cognitive technologies for mathematics education. In: Schoenfeld A (ed) Cognitive science and mathematics education. Lawrence Erlbaum Associates Inc, Hillsdale, NJ, pp 89–122

Pea RD, Kurland DM (1984) On the cognitive effects of learning computer programming. New Ideas Psychol 2(2):137–168

Penner DE (2000) Explaining systems: investigating middle school students’ understanding of emergent phenomena. J Res Sci Teach 37(8):784–806

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):117–166

Portsmore M (2005) ROBOLAB: intuitive robotic programming software to support lifelong learning. Apple learning technology review. Spring/Summer, 2005

Resnick M (2003) Thinking like a tree (and other forms of ecological thinking). Int J Comput Math Learn 8(1):43–62

Resnick M, Ocko S, Papert S (1988) LEGO, logo, and design. Child Environ Q 5(4):14–18

Resnick M, Wilensky U (1998) Diving into complexity: developing probabilistic decentralized thinking through role-playing activities. J Learn Sci 7(2):153–172

Schoenfeld AH (1992) Learning to think mathematically: problem solving, metacognition, and sense making in mathematics. Handbook of research on mathematics teaching and learning, pp 334–370

Schunk DH (1983) Ability versus effort attributional feedback: differential effects on self-efficacy and achievement. J Educ Psychol 75(6):848

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 72–75

Sengupta P, Wilensky U (2009) Learning electricity with NIELS: thinking with electrons and thinking in levels. Int J Comput Math Learn 14(1):21–50

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 perspective

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, USA

Sklar E, Eguchi A, Johnson J (2003a) RoboCupJunior: learning with educational robotics. RoboCup 2002: robot soccer world cup VI, pp 238–253

Sklar E, Parsons S, Stone P (2003b) Robocup in higher education: a preliminary report. In: Proceedings of the 7th RoboCup symposium

Soloway E (1986) Learning to program = learning to construct mechanisms and explanations. Commun ACM 29(9):850–858

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):1–4

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):171–209

Wilensky U, Resnick M (1999) Thinking in levels: a dynamic systems perspective to making sense of the world. J Sci Educ Technol 8(1):3–19

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, CA

Wilensky U, Stroup W (1999b) HubNet [Computer software]. Northwestern University, Center for Connected Learning and Computer-Based Modeling, Evanston, IL

Wing JM (2006) Computational thinking. Commun ACM 49(3):33–35

Wolfram S (2002) A new kind of science. Wolfram Media, Champaign, IL

Wyeth P (2008) How young children learn to program with sensor, action, and logic blocks. J Learn Sci 17(4):517–550