Grover & Pea start us off by towards towards a definition of CT (Computational Thinking). Many tasks and features of CT are listed, but the core revolves around information process and developing knowledge in constructive ways using data and programming skills. A couple classes ago we talked a lot about programming as a creative or artisitic activity, and Grover & Pea make two key points in the introduction that zero in on this: 1. Computing is a creative human activity, and 5. Programming is a creative process that produces computational artifacts. There is a lot of value, I think, to presenting CT as a creative process, a means by which the student (or programmer, engineering, whoever) creates something from their own mind and then has practical purpose. Offering this to K-12 students may be a strong foundation for the framework. Grover & Pea then move onto discuss some of the difficulties of this introduction of CT to K-12 classrooms, not the least of which being assessing CT, involving girls in CT, and finding the adequate resources to bring students along an introductory level of CT. This is where a lot of the matieral from Sengupta, et al.’s paper helps by theorizing an effective framework for integrating CT.
An early concept discussed in that paper is abstraction and generalization. The creative skills needed to effectively apply CT requires a mastery of abstraction and imaginative visualization, especially when planning CT solutions which is a core skill. This is the adhesive that holds together the Model of Scientific Phenomenon, which is visualized on page 11 of our PDF document. The visualization well summarizes the continuous and cyclical nature of scientific discovery. Beginning at the top of the visualization, the curiousity of Scientific Inquiry (an innate human trait) leads students to seek knowledge on an initial basis. To best grasp the significance of this knowledge, the student seeks the design of algorithm, or the constructs of programming as it is conceived to best relate to the phenomena. As the algorithmic models continue to improve and garner practicality, the application of these algorithms now take the form of engineering and simulation. This experience immerses the student in a ‘deepening of conceptual understanding’ and the students begins the cycle again by seeking deeper scientific truth. This is a principled approach, allowing us to view CT as the vehicle by which students move from seeking understanding to applying their understanding in practical ways.
The Sengupta et al. article also lays framework for self-evaluation and comparison by the students as they model their own CT, by providing the expert models that can reveal to the students what methods and techniques of CT are most adequate. Providing the students with a great volume of quality CT tools is essential for developing these creative skills—just children role models of CT just as role models are helpful in anything else. The prospects of integrating CT on deeper levels than ever before is very exciting.