In chapter 12, "New Paradigms for Computing, New Paradigms for Thinking," Mitchel Resnick argues that new computational paradigms (such as object- oriented programming and parallelism) can significantly influence not only how people use computers, but how they make sense of the world. In particular, he describes a parallel-programming language called StarLogo that he designed to help students explore decentralized systems such as bird flocks, traffic jams, and market economies. By building models with StarLogo, students can move beyond the "centralized mindset" and develop a deeper understanding of decentralized systems.
Uri Wilensky (chapter 13) directs StarLogo toward a different goal: to help learners develop their intuitive conceptions of probabilistic ideas. In the chapter, "Making Sense of Probability Through Paradox and Programming," Wilensky presents a case study of a learner who uses programming to help resolve a probability paradox, and in the process develops stronger intuitions about randomness and distribution--and the connections between them. Wilensky's study illustrates that the primary obstacles to learning probability are conceptual and epistemological, and it shows how programming can play a powerful role in learning mathematics by making hidden assumptions explicit and concrete.
In the final chapter, "Ideal and Real Systems," Fred Martin probes students' thinking about systems in the context of a robot-design competition that he helped to design for MIT undergraduates. Martin analyzes how and why undergraduates have trouble developing effective strategies for controlling their robots. He shows that students tend to build robots that perform properly only under ideal conditions, not in the "messiness" of the real world. Martin calls for a change in undergraduate engineering education, arguing that the standard curriculum encourages design strategies that are not appropriate for many real-world technological systems.
This book would not have been possible without the help of many people. All of the authors made major contributions of time, thought, and energy. Our current work benefits from the past and continuing contributions of the extended Logo community, including many former members of the Epistemology and Learning Group at the MIT Media Lab. We are grateful to Wanda Gleason and Florence D. Williams for helping with many organizational and editing tasks, and to Jacqueline Karaaslanian and Mai Cleary for providing general administrative support. The National Science Foundation (Grants 9153719-MDR and 9358519- RED), the LEGO Group, IBM, Nintendo Inc., and the Media Lab's News in the Future consortium have generously supported this research. Finally, we thank Seymour Papert for inspiring all of us to think in new ways about learning, children, minds, and computers.