At the centre of the methodology used in this book is STEM learning variability space that includes STEM pedagogical variability, learners' social variability, technological variability, CS content variability and interaction variability. To design smart components, firstly, the STEM learning variability space is defined for each component separately, and then model-driven approaches are applied. The theoretical basis includes feature-based modelling and model transformations at the top specification level and heterogeneous meta-programming techniques at the implementation level. Practice includes multiple case studies oriented for solving the task prototypes, taken from the real world, by educational robots. These case studies illustrate the process of gaining interdisciplinary knowledge pieces identified as S-knowledge, T-knowledge, E-knowledge, M-knowledge or integrated STEM knowledge and evaluate smart components from the pedagogical and technological perspectives based on data gathered from one real teaching setting. Smart STEM-Driven Computer Science Education: Theory, Methodology and Robot-based Practices outlines the overall capabilities of the proposed approach and also points out the drawbacks from the viewpoint of different actors, i.e. researchers, designers, teachers and learners.

"Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques" presents an overall analysis of meta-programming, focusing on insights of meta-programming techniques, heterogeneous meta-program development processes in the context of model-driven, feature-based and transformative approaches.

The fundamental concepts of meta-programming are still not thoroughly understood, in this well organized book divided into three parts the authors help to address this. Chapters include: Taxonomy of fundamental concepts of meta-programming; Concept of structural heterogeneous meta-programming based on the original meta-language; Model-driven concept and feature-based modeling to the development process of meta-programs; Equivalent meta-program transformations and metrics to evaluate complexity of feature-based models and meta-programs; Variety of academic research case studies within different application domains to experimentally verify the soundness of the investigated approaches.

Both authors are professors at Kaunas University of Technology with 15 years research and teaching experience in the field. "Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques" is aimed at post-graduates in computer science and software engineering and researchers and program system developers wishing to extend their knowledge in this rapidly evolving sector of science and technology.

This monograph presents the challenges, vision and context to design smart learning objects (SLOs) through Computer Science (CS) education modelling and feature model transformations. It presents the latest research on the meta-programming-based generative learning objects (the latter with advanced features are treated as SLOs) and the use of educational robots in teaching CS topics. The introduced methodology includes the overall processes to develop SLO and smart educational environment (SEE) and integrates both into the real education setting to provide teaching in CS using constructivist and project-based approaches along with evaluation of pedagogic outcomes. Smart Learning Objects for Smart Education in Computer Science will appeal to researchers in CS education particularly those interested in using robots in teaching, course designers and educational software and tools developers. With research and exercise questions at the end of each chapter students studying CS related courses will find this work informative and valuable too.

Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques presents an overall analysis of meta-programming, focusing on insights of meta-programming techniques, heterogeneous meta-program development processes in the context of model-driven, feature-based and transformative approaches. The fundamental concepts of meta-programming are still not thoroughly understood, in this well organized book divided into three parts the authors help to address this. Chapters include: Taxonomy of fundamental concepts of meta-programming; Concept of structural heterogeneous meta-programming based on the original meta-language; Model-driven concept and feature-based modeling to the development process of meta-programs; Equivalent meta-program transformations and metrics to evaluate complexity of feature-based models and meta-programs; Variety of academic research case studies within different application domains to experimentally verify the soundness of the investigated approaches. Both authors are professors at Kaunas University of Technology with 15 years research and teaching experience in the field. Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques is aimed at post-graduates in computer science and software engineering and researchers and program system developers wishing to extend their knowledge in this rapidly evolving sector of science and technology.

At the centre of the methodology used in this book is STEM learning variability space that includes STEM pedagogical variability, learners' social variability, technological variability, CS content variability and interaction variability. To design smart components, firstly, the STEM learning variability space is defined for each component separately, and then model-driven approaches are applied. The theoretical basis includes feature-based modelling and model transformations at the top specification level and heterogeneous meta-programming techniques at the implementation level. Practice includes multiple case studies oriented for solving the task prototypes, taken from the real world, by educational robots. These case studies illustrate the process of gaining interdisciplinary knowledge pieces identified as S-knowledge, T-knowledge, E-knowledge, M-knowledge or integrated STEM knowledge and evaluate smart components from the pedagogical and technological perspectives based on data gathered from one real teaching setting. Smart STEM-Driven Computer Science Education: Theory, Methodology and Robot-based Practices outlines the overall capabilities of the proposed approach and also points out the drawbacks from the viewpoint of different actors, i.e. researchers, designers, teachers and learners.