Sengupta et al’s article sought to describe computational thinking as a scientific process that also involves inquiry and problem solving. Computational thinking was also described as a science as a practice theory, which includes modeling and representational forms. The article placed computational thinking into science and math standards, citing that their similar cognitive processes for reasoning.
Grover and Pea’s article described computational thinking as information processing so that a solution may be sought out by a processing agent or through a set of instructions. The National Science Foundation has listed seven big ideas for computational thinking. The last big idea says that computing enables innovation in other fields or subject areas. Effective pedagogical approaches to computational thinking are still being developed; curriculum and assessment are still being developed.
Between both articles computational thinking was described as a thought process that describes problems such that they may be solved simplistically, such as by an algorithm. Computational thinking should be described as a science as it involves inquiry and creation of ideas and artifacts. Also, both articles ask important questions such as what is the best pedagogical approach to computational thinking and how can instructors scaffold computer science so that the content is challenging for students with little to large background knowledge. Both articles agree that pedagogical approaches to computational thinking are still being developed and that the most effective ways are still yet to be sought out. Computational thinking has a great importance in how frequently technology is used currently. Both articles stressed the importance of preparing students to be problem solvers; computational thinking develops this skill set as a science.