The 10 Skill Areas
The Computational Thinking Assessment is comprised of over 250 objectives, encompassing 10 skill areas that span across the range of aptitudes that the pursuit of computational thinking knowledge can develop within students. Although not fully inclusive of all covered topics, the statements below help provide an overview introduction on what each skill area targets. As can be seen from the descriptions below, computational thinking expands beyond traditional core programming curriculum to develop skill sets that are widely applicable towards logically solving a diversity of real-world problems. Regardless of whether a student pursues a career in computer science or even a STEM field, it is easy to see how the skill areas as described below are readily applicable to wide variety of careers.
Challenge Definition
Understanding what makes for a valid solution. What are all the use cases that must be handled and under what conditions. What qualifies as success for all stakeholders.
Performance Definition
How are various solutions evaluated from all stakeholders’ perspectives, thereby make valued design decisions and trade-offs in a well justified and objective manner. How variations in one aspect of a problem/solution, such as inputs, could affect another aspect such as outputs.
Abstraction
How to represent a real-world problem in its simplest complete form with an understanding of the kinds of functionality that must be achieved and the governing rules of the problem.
Fundamental Programming
Demonstration of core computer science and programming skills, including good coding practices and basic architectural concepts.
Algorithm Development
Create and evaluate ordered series of steps towards solving problems (algorithms) while considering design specifications on inputs, outputs, interfaces to other code / devices, performance, code efficiency, etc. Demonstrate and be able classic algorithmic approaches, how to split complex problems into combinations of simpler algorithms and be able to recognize algorithmic approaches in others work.
Data Analysis
Review sets of data to extract additional meaning. Recognize patterns and trends in data. Create algorithms that utilize data sets to validate or disprove hypothesis as well as make decisions.
Development & Planning
Determine sets of tasks, relative estimates on the expected task effort, and means for verifying task competition. Describe how tasks will combine to create a complete functioning solution.
Team Collaboration
Demonstration of how to work effectively in teams towards solving a complex problem. Show how to review others work as well as make use of received feedback. How to split a solution’s development into multiple parts and be able to combine solution parts, including parts from past projects, effectively.
Communication
Development of proper internal and external documentation to describe code capabilities and limitations, as well as justify design decisions. Enhance code readability, utilize pseudocode and flowcharts, and make effective use of visuals to convey design approaches.
Testing, Analysis & Debugging
Determine if and where within a solution approach may produce an undesired result. Determine what tests could be run to help identify the cause of an undesired result as well as how to interpret the results of tests run for this purpose. Determine what corrective actions should be taken to eliminate identified issues.