tationally realizes that data-intensive problems such as sequencing DNA may be amenable to solutions based on algorithms and automation. Third, information technology can often be used to help manage complexity in understanding complicated problems. A person thinking computationally realizes that computational modeling can help address complex problems across varied disciplines such as climate change, economic policy, and educational decision making.

Responding to the workshop focus on explicating the scope and nature of computational thinking (with the implied goal of being more effective in imparting to students the essentials of computational thinking), Uri Wilensky offered a caution—that “it is not necessarily the case that the best way to enter into something is to enter it in the way that an expert already understands it.” For those in attendance at the workshop, he noted that “if one is already an expert in computer science, it’s easy to forget what it’s like to enter into the field.” He did not argue that the explication effort was wasted or inappropriate, only that as a community “we should be careful about the process of bringing a lot of people, in a widespread way, into computational thinking. We should do more than present to students expert ways of thinking computationally—attention must be paid to the developmental understanding of students.” Roy Pea made a similar point when he cautioned workshop participants against focusing on the prototypes for computational thinking provided by experts in the field, because such prototypes “may lead us away from the professed goal of everyday computational thinking.”


Over the course of the workshop discussion, several participants described computational thinking as a collection of mental tools and concepts from computer science that help people to solve problems, design systems, and understand human behavior. For example, Wing drew the distinction between “metal tools” and “mental tools,” the former being the hardware/software applications that help solve problems and the latter being cognitive and intellectual skills that human beings can use to understand and solve problems more effectively. Participants argued that these concepts feature prominently in computer science but are not exclusive to the field.

Computational thinking was defined in a number of ways. These definitions fell into several categories and are described (in no particular order) below:

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