Promoting computational thinking is one of the top priorities in CS education as well as in other STEM and non-STEM disciplines.Our innovative NSF-funded IC2Think project blends computational thinking with creative thinking so that students leverage their creative thinking skills to "unlock" their understanding of computational thinking. In Fall 2012, we deployed creative exercises designed to engage Epstein's creative competencies (Surrounding, Capturing, Challenging and Broadening) in introductory level CS courses targeting four different groups (CS, engineering, combined CS/physical sciences, and humanities majors). Students combined hands-on problem solving with guided analysis and reflection to connect their creative activities to CS topics such as conditionals and arrays and to real-world CS applications. Evaluation results (approximately 150 students) found that creative thinking exercise completion had a linear "dosage" effect. As students completed more exercises [0/1 -4], they increased their long-term retention [a computational thinking test], F(3, 98) = 4.76, p =.004, partial Eta 2 = .127 and course grades, F(3, 109) = 4.32, p =.006, partial Eta 2 = .106. These findings support our belief that the addition of creative thinking exercises to CSCE courses improves the learning of computational knowledge and skills.

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The need for more post-secondary students to major and graduate in STEM fields is widely recognized. Students' motivation and strategic self-regulation have been identified as playing crucial roles in their success in STEM classes. But, how students' strategy use, self-regulation, knowledge building, and engagement impact different learning outcomes is not well understood. Our goal in this study was to investigate how motivation, strategic self-regulation, and creative competency were associated with course achievement and long-term learning of computational thinking knowledge and skills in introductory computer science courses. Student grades and long-term retention were positively associated with self-regulated strategy use and knowledge building, and negatively associated with lack of regulation. Grades were associated with higher study effort and knowledge retention was associated with higher study time. For motivation, higher learning-and task-approach goal orientations, endogenous instrumentality, and positive affect and lower learning-, task-, and performance-avoid goal orientations, exogenous instrumentality and negative affect were associated with higher grades and knowledge retention and also with strategic selfregulation and engagement. Implicit intelligence beliefs were associated with strategic self-regulation, but not grades or knowledge retention. Creative competency was associated with knowledge retention, but not grades, and with higher strategic self-regulation. Implications for STEM education are discussed.

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This chapter presents an “algorithmic criticism,” which seeks, in the narrowing forces of constraint embodied and instantiated in the strictures of programming, an analogue to the liberating potentialities of art. It proposes that we create tools—practical, instrumental, verifiable mechanisms—that enable critical engagement, interpretation, conversation, and contemplation. The chapter furthermore proposes that we channel the heightened objectivity made possible by the machine into the cultivation of those heightened subjectivities necessary for critical work. Moreover, this chapter argues that scientific method and metaphor (or, more precisely, the use of these notions within the distorted epistemology we call “scientism”) is, for the most part, incompatible with the terms of humanistic endeavor.