This paper describes the design and initial assessment of a general education course in computational thinking for non-computer science majors. The key elements of the course include multidisciplinary cohorts to achieve learning across contexts, multiple languages/tools, including block-based and textual programming languages, repeated exposure to the underlying computational ideas in different forms, and student-defined projects using real world ("big") data to heighten motivation through self-directed contextualized learning. The preliminary multi-methods assessment shows that the course engendered high levels of motivation, achieved key objectives for learning in and across contexts, largely affirmed the choice of languages/tools, and supported, though less strongly than anticipated, the motivational effects of real-world data.

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In this paper I discuss my doctoral research which aims to better understand collaborative computational thinking (CT). In order to be successful in the 21 st century, skills such CT (understanding and applying computational concepts) are indispensable for people across all ages and disciplines. One can learn computational concepts by taking a traditional course offered in a school or by self-guided learning through an online platform. Whatever the form of learning, computational concepts can be difficult to fully comprehend for novices. Collaborative learning has been considered effective in reducing learner's anxiety and in helping struggling learners overcome common learning difficulties. The proposed dissertation study aims to investigate how collaboration impacts learning of CT across both classroom setting and in an online learning community. This paper briefly describes the motivation and outline of my proposed dissertation study, the overarching research questions, the data currently collected, and my data analysis methodologies.