It does not use drag and drop blocks like NXT-G or LabView - instead, it uses text to command (and conquer) the robot.In this paper, we introduce a methodology to design robot-oriented generative learning objects (GLOs) that are, in fact, heterogeneous meta-programs to teach computer science (CS) topics such as programming. RobotC is a text-based programming language. Online courses and programs are designed to introduce you to each of these areas and jump start your career in this exciting and rapidly expanding field.RobotC is a programming language used to program robots participating in FTC competitions. Robotics courses cover multiple science, linear math and technology disciplines including machine learning, artificial intelligence, data science, design and engineering.
The GLO design task is formulated as mapping the problem domain model on the solution domain model. Both are represented by feature models. Meta-programming is considered as a solution domain. Therefore, the CS learning variability represents the problem domain. By learning variability we mean the attributes of the framework extracted and represented as feature models with multiple values. Firstly, we define the CS learning domain using the known educational framework TPACK (Technology, Pedagogy And Content Knowledge).
The tool enables to improve the GLO design process significantly (in terms of time and quality) and to achieve a higher quality and functionality of GLOs themselves (in terms of the parameter space enlargement for reuse and adaptation). We present the architecture and some characteristics of the tool. Its theoretical background includes: (a) a formal definition of feature-based models (b) a graph-based and set-based definition of meta-programming concepts (c) transformation rules to support the model mapping (d) a computational Abstract State Machine model to define the processes and design tool for developing GLOs. The multi-level separation of concepts, model representation and transformation forms the conceptual background.
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