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. Front Matter ....Pages i-xvii Front Matter ....Pages 1-1 Challenges of STEM-Driven Computer Science (CS) Education (Vytautas Štuikys, Renata Burbaitė)....Pages 3-29 A Vision for Introducing STEM into CS Education at School (Vytautas Štuikys, Renata Burbaitė)....Pages 31-55 Smart Devices and Educational Robotics as Technology for STEM Knowledge (Vytautas Štuikys, Renata Burbaitė)....Pages 57-67 Front Matter ....Pages 69-70 A Methodological Background for STEM-Driven Reuse-Enhanced CS Education (Vytautas Štuikys, Renata Burbaitė)....Pages 71-98 Theoretical Background to Implement STEM-Driven Approaches (Vytautas Štuikys, Renata Burbaitė)....Pages 99-132 Front Matter ....Pages 133-133 Understanding of Smart Content for STEM-Driven CS Education (Vytautas Štuikys, Renata Burbaitė)....Pages 135-155 Model-Driven Design and Redesign of Smart STEM-Driven CS Content (Vytautas Štuikys, Renata Burbaitė)....Pages 157-187 Stage-Based Smart Learning Objects: Adaptation Perspective (Vytautas Štuikys, Renata Burbaitė)....Pages 189-216 Agent-Based GLOs/SLOs for STEM (Vytautas Štuikys, Renata Burbaitė)....Pages 217-229 Front Matter ....Pages 231-231 Personal Generative Library for STEM-Driven Educational Resources (Vytautas Štuikys, Renata Burbaitė)....Pages 233-257 A Methodology and Tools for Creating Generative Scenario for STEM (Vytautas Štuikys, Renata Burbaitė)....Pages 259-278 Smart STEM-Driven Educational Environment for CS Education: A Case Study (Vytautas Štuikys, Renata Burbaitė)....Pages 279-303 Practice of Smart STEM-Driven CS Education at High School (Vytautas Štuikys, Renata Burbaitė)....Pages 305-324 Front Matter ....Pages 325-325 Internet-of-Things: A New Vision for STEM and CS Education (Vytautas Štuikys, Renata Burbaitė)....Pages 327-345 A Finalizing Discussion and Open Issues (Vytautas Štuikys, Renata Burbaitė)....Pages 347-363 Back Matter ....Pages 365-368 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.
