Programming is, as mentioned, an important area to train logical and structured thinking. The basic building blocks of automation are covered: sequencing actions, defining repeated sections and decision making based on perceptions. These are the ingredients that can be written down in a formal way. In later stages, you learn to analyze the choices made in programming languages.

Fundamental parts of mathematics are naturally covered when doing exercises. By writing down under which conditions an action should be performed, logic is introduced. The robot world demonstrates geometry in various navigational tasks. When rewriting a solution, algebraic rules will guide you to do it correctly. Random processes are at the basis of many modern applications, from banking to developing medicines. With RoboMind you can observe this yourself.

Problem solving is a another core concept of Computational Thinking. First a problem needs to be analysed in order to come to a precise definition of the goal. Then a possible solution for the problem needs to be found. Possible solutions are evaluated in terms of generality and complexity and can be tried by letting a machine execute it. We train you in this problem solving process in a rigorous way and introduce classical strategies along the way.

Creating solutions in the form of writing a program teaches you many skills: from carefully analyzing a challenge to design, implemention and testing of your solutions.

Big challenges are rarely solved by an individual. Communication skills are trained in demanding problems. From brainstorming sessions, to delegating tasks and presenting results.

Computational Thinking is directly connected to relevant applications in many areas. The robot world is a perfect fit to demonstrate transportation challenges, automation in factories, and searching in an unknown environment.

Understanding what you learned in theory, during the implementation process and with concrete applications, will allow you to formulate design decisions. By evaluating different solutions, specific choices for an application domain will reinforce these decisions. Different application areas of the same core concepts stimulate seeing patterns across domains.