Smart Learning Objects For Smart Education In Computer Science
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Smart Learning Objects for Smart Education in Computer Science
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.
Cases on Smart Learning Environments
At a time when ICTs are proliferating various facets of society and human interactivity, optimizing the use of these tools and technologies not only enhances learning but also transforms learning experiences all together, resulting in an increase of effectiveness and quality of education around the globe. As such, teachers are being challenged to implement a wide range of tools, such as mobile learning and augmented reality, to create smarter learning environments inside and outside of the classroom. Cases on Smart Learning Environments explores the potential of SLE tools for enhanced learning outcomes as experienced by educators, learners, and administrators from various learning institutions around the world. This publication presents cases on the real-world implementation of SLEs in 11 countries that span the continents of Asia, Africa, Europe, and North and South America. Featuring coverage on a broad range of topics such as learner engagement, teacher training, and intelligent agent technology, this book is ideally designed for academicians, instructors, instructional designers, librarians, educational stakeholders, and curriculum developers.
Smart STEM-Driven Computer Science Education
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.