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020 _a9783031482359
_9978-3-031-48235-9
024 7 _a10.1007/978-3-031-48235-9
_2doi
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072 7 _aCOM004000
_2bisacsh
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_223
100 1 _aŠtuikys, Vytautas.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aEvolution of STEM-Driven Computer Science Education
_h[electronic resource] :
_bThe Perspective of Big Concepts /
_cby Vytautas Štuikys, Renata Burbaitė.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVI, 360 p. 83 illus., 39 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aContext and model for writing this book: An idea of big concepts -- Part 1: Pedagogical aspects of STEM-driven CS education evolution: Integrated STEM-CS Skills model, personalisation aspects and collaborative learning -- Models for the development and assessment of Integrated STEM (ISTEM) Skills: A case study -- Enforcing STEM-driven CS education through personalisation -- Personal generative libraries for personalised learning: A case study -- Enforcing STEM-driven CS education through collaborative learning -- Part 2: Internet of Things (IoT) and Data Science (DS) concepts in K-12 STEM-driven CS education.-Methodological aspects of educational internet of things -- Multi-stage prototyping for introducing IoT concepts: A case study -- Introducing data science concepts into STEM-driven computer science education -- Part 3: Introduction to artificial intelligence -- A vision for introducing AI topics: A case study -- Speech recognition technology in K-12 STEM-driven computer science education -- Introduction to artificial neural networks and machine learning -- Overall evaluation of this book concepts and approaches.
520 _aThe book discusses the evolution of STEM-driven Computer Science (CS) Education based on three categories of Big Concepts, Smart Education (Pedagogy), Technology (tools and adequate processes) and Content that relates to IoT, Data Science and AI. For developing, designing, testing, delivering and assessing learning outcomes for K-12 students (9-12 classes), the multi-dimensional modelling methodology is at the centre. The methodology covers conceptual and feature-based modelling, prototyping, and virtual and physical modelling at the implementation and usage level. Chapters contain case studies to assist understanding and learning. The book contains multiple methodological and scientific innovations including models, frameworks and approaches to drive STEM-driven CS education evolution. Educational strategists, educators, and researchers will find valuable material in this book to help them improve STEM-drivenCS education strategies, curriculum development, and new ideas for research. .
650 0 _aComputational intelligence.
650 0 _aEducation
_xData processing.
650 0 _aSoftware engineering.
650 0 _aRobotics.
650 1 4 _aComputational Intelligence.
650 2 4 _aComputers and Education.
650 2 4 _aSoftware Engineering.
650 2 4 _aRobotics.
700 1 _aBurbaitė, Renata.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031482342
776 0 8 _iPrinted edition:
_z9783031482366
776 0 8 _iPrinted edition:
_z9783031482373
856 4 0 _uhttps://doi.org/10.1007/978-3-031-48235-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c187219
_d187219