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1. Computational Thinking Frameworks used in Computational Thinking Assessment in Higher Education. A Systematized Literature Review.

   https://doi.org/10.18260/1-2--36824  

   Cruz Castro, Laura; Shoaib, Huma; Douglas, Kerrie (January 2021, ASEE Annual Conference)

   null (Ed.)

   This research paper presents a literature review of Computational Thinking (CT) frameworks and assessment practices. CT is a 21st century way of solving a problem. It refers specifically to the methods that are effective when trying to solve a problem with a machine or other computational tools. In the past few years, CT researchers and educationists' significant movement started to look for a formal definition and composition of CT in K-12 and higher education. From this effort, over 20 different definitions and frameworks for CT have emerged. Although the availability of literature on CT has been increasing over the last decade, there is limited research synthesis available on how to assess CT better. Besides, it is known that in higher education designing assessments for CT is challenging and one of the primary reasons is that the precise meaning of CT is still unknown. This research paper, therefore, presents a systematized literature review on CT frameworks and assessment practice. We search three different databases and review 19 journal articles that address the assessment of CT in higher education to answer the following two research questions: 1) What does the literature inform us about practices and types of assessments used to evaluate CT in higher education? 2) Which frameworks of CT are present in literature to support CT assessment in higher education? The critical components of this review focus on frameworks and assessment practices based on CT. We develop a synthesis of suggestions and explanations to answer the proposed questions based on literature from recent research in CT. Based on our initial synthesis, we found a disconnect between theory and practice. Specifically, neither the ideas within CT frameworks nor those from CT assessment research are being utilized by the other. Therefore, there is a dire need to connect the two for practical implementation and further research in CT in higher education. 

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2. Vectors of CT-ification: Integrating Computational Activities in STEM Classrooms.

   https://doi.org/10.1145/3328778.3372674  

   Bain, C. & (April 2020, Proceedings of tThe 51st ACM Technical Symposium on Computer Science Education)

   null (Ed.)

   While the Next Generation Science Standards set an expectation for developing computer science and computational thinking (CT) practices in the context of science subjects, it is an open question as to how to create curriculum and assessments that develop and measure these practices. In this poster, we show one possible solution to this problem: to introduce students to computer science through infusing computational thinking practices ("CT-ifying") science classrooms. To address this gap, our group has worked to explicitly characterize core CT-STEM practices as specific learning objectives and we use these to guide our development of science curriculum and assessments. However, having these learning objectives in mind is not enough to actually create activities that engage students in CT practices. We have developed along with science teachers, a strategy of examining a teacher’s existing curricula and identifying potential activities and concepts to “CT-ify”, rather than creating entirely new curricula from scratch by using the concept of scale as an “attack vector” to design science units that integrate computational thinking practices into traditional science curricula. We demonstrate how we conceptualize four different versions of scale in science, 1. Time, 2. Size, 3. Number, and 4. Repeatability. We also present examples of these concepts in traditional high school science curricula that hundreds of students in a large urban US school district have used. 

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3. A DSML for a Robotics Environment to Support Synergistic Learning of CT and Geometry. Kong, S. C., Sheldon, J., & Li, K. Y.. (Eds.). Conference

   Hutchins, N. (July 2018, Proceedings of International Conference on Computational Thinking Education 2018.)

   Synergistic learning of computational thinking (CT) and STEM has proven to be an effective method for enhancing CT education as well as advancing learning in many STEM domains. Domain Specific Modeling Languages (DSML) facilitate the building of computational modeling frameworks that are directly linked to STEM content, thus making it easier for students to focus on concepts and practices. At the same time, teachers can more easily relate curricular content to the model building tasks. This paper discusses the design, development, and implementation of a robotics DSML to support a middle school geometry curriculum. 

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4. Integrating Computational Modeling in K-12 STEM Classrooms

   https://doi.org/10.1145/3287324.3293757  

   Biswas, Gautam; Hutchins, Nicole; Lédeczi, Ákos; Grover, Shuchi; Basu, Satabdi (February 2019, Proceedings of the 50th ACM Technical Symposium on Computer Science Education)

   C2STEM is a web-based learning environment founded on a novel paradigm that combines block-structured, visual programming with the concept of domain specific modeling languages (DSMLs) to promote the synergistic learning of discipline-specific and computational thinking (CT) concepts and practices. Our design-based, collaborative learning environment aims to provide students in K-12 classrooms with immersive experiences in CT through computational modeling in realistic scenarios (e.g., building models of scientific phenomena). The goal is to increase student engagement and include inclusive opportunities for developing key computational skills needed for the 21st century workforce. Research implementations that include a semester-long high school physics classroom study have demonstrated the effectiveness of our approach in supporting synergistic learning of STEM and CS/CT concepts and practices, especially when compared to a traditional classroom approach. This technology demonstration will showcase our CS+X (X = physics, marine biology, or earth science) learning environment and associated curricula. Participants can engage in our design process and learn how to develop curricular modules that cover STEM and CS/CT concepts and practices. Our work is supported by an NSF STEM+C grant and involves a multi-institutional team comprising Vanderbilt University, SRI International, Looking Glass Ventures, Stanford University, Salem State University, and ETR. More information, including example computational modeling tasks, can be found at C2STEM.org. 

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5. Seeds of (r)Evolution: Constructionism Co-Design with High School Science Teachers

   Kelter, J.; Peel, A.; Bain, C.; Anton, G.; Dabholkar, S.; Aslan, U.; Horn, M.S.; Wilensky, U. (June 2020, Constructionism 2020)

   In the decades since Papert published Mindstorms (1980), computation has transformed nearly every branch of scientific practice. Accordingly, there is increasing recognition that computation and computational thinking (CT) must be a core part of STEM education in a broad range of subjects. Previous work has demonstrated the efficacy of incorporating computation into STEM courses and introduced a taxonomy of CT practices in STEM. However, this work rarely involved teachers as more than implementers of units designed by researchers. In The Children’s Machine, Papert asked “What can be done to mobilize the potential force for change inherent in the position of teachers?” (Papert, 1994, pg. 79). We argue that involving teachers as co-design partners supports them to be cultural change agents in education. We report here on the first phase of a research project in which we worked with STEM educators to co-design curricular science units that incorporate computational thinking and practices. Eight high school teachers and one university professor joined nine members of our research team for a month-long Computational Thinking Summer Institute (CTSI). The co-design process was a constructionist design and learning experience for both the teachers and researchers. We focus here on understanding the co-design process and its implications for teachers by asking: (1) How did teachers shift in their attitudes and confidence regarding CT? (2) What different co-design styles emerged and did any tensions arise? Generally, we found that teachers gained confidence and skills in CT and computational tools over the course of the summer. Only one teacher reported a decrease in confidence in one aspect of CT (computational modeling), but this seemed to result from gaining a broader and more nuanced understanding of this rich area. A range of co-design styles emerged over the summer. Some teachers chose to focus on designing the curriculum and advising on the computational tools to be used in it, while leaving the construction of those tools to their co-designers. Other teachers actively participated in constructing models and computational tools themselves. The pluralism of co-design styles allowed teachers of various comfort levels with computation to meaningfully contribute to a computationally enhanced constructionist curriculum. However, it also led to a tension for some teachers between working to finish their curriculum versus gaining experience with computational tools. In the time crunch to complete their unit during CTSI, some teachers chose to save time by working on the curriculum while their co-design partners (researchers) created the supporting computational tools. These teachers still grew in their computational sophistication, but they could not devote as much time as they wanted to their own computational learning. 

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