04728nam 22005175 450 991029928420332120200703114311.03-319-78485-410.1007/978-3-319-78485-4(CKB)3810000000358761(MiAaPQ)EBC5441106(DE-He213)978-3-319-78485-4(PPN)229497594(EXLCZ)99381000000035876120180628d2018 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierSmart STEM-Driven Computer Science Education Theory, Methodology and Robot-based Practices /by Vytautas Štuikys, Renata Burbaitė1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (370 pages)3-319-78484-6 Part 1: Introduction: Motivation, Challenges, and Conceptual Vision of STEM-Driven CS Education Based on Robotics -- Challenges of STEM-Driven Computer Science (CS) Education -- A Vision for Introducing STEM into CS Education at School -- Smart Devices and Educational Robotics as Technology for STEM Knowledge -- Part 2: Methodological and Theoretical Background of Approaches to Implement the Proposed Vision -- A Methodological Background for STEM-Driven Reuse-Enhanced CS Education 60 -- Theoretical Background to Implement STEM-Driven Approaches -- Part 3: Design, Re-Design and Use of Smart Content for STEM-Driven CS Education -- Understanding of Smart Content for STEM-Driven CS Education -- Model-Driven Design and Re-Design of Smart STEM-Driven CS Content -- Stage-Based Smart Learning Objects: Adaptation Perspective -- Agent-Based GLOs/SLOs for STEM -- Part 4: Infrastructure to Support STEM-Driven CS Educational Practice -- Personal Generative Library for STEM-Driven Educational Resources -- A Methodology and Tools for Creating Generative Scenario for STEM -- Smart STEM-Driven Educational Environment for CS Education: A Case Study -- Practice of Smart STEM-Driven CS Education at High School -- Part 5:An Extended Vision to STEM-Driven CS Education.-Internet-of-Things: A New Vision for STEM and CS Education -- A Finalizing Discussion and Open Issues -- Glossary -- Indexes.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.Computers, Special purposeEducational technologyArtificial intelligenceSpecial Purpose and Application-Based Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/I13030Educational Technologyhttps://scigraph.springernature.com/ontologies/product-market-codes/O21000Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computers, Special purpose.Educational technology.Artificial intelligence.Special Purpose and Application-Based Systems.Educational Technology.Artificial Intelligence.004.071Štuikys Vytautasauthttp://id.loc.gov/vocabulary/relators/aut875500Burbaitė Renataauthttp://id.loc.gov/vocabulary/relators/autBOOK9910299284203321Smart STEM-Driven Computer Science Education1954799UNINA