LEADER 04135nam  22006375  450 
001 9910799493703321
005 20250807153224.0
010   $a3-031-48235-2
024 7 $a10.1007/978-3-031-48235-9
035   $a(CKB)29526935300041
035   $a(MiAaPQ)EBC31071252
035   $a(Au-PeEL)EBL31071252
035   $a(DE-He213)978-3-031-48235-9
035   $a(OCoLC)1417158785
035   $a(EXLCZ)9929526935300041
100   $a20231227d2024      u|         0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aEvolution of STEM-Driven Computer Science Education $eThe Perspective of Big Concepts /$fby Vytautas ?tuikys, Renata Burbait?
205   $a1st ed. 2024.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024.
215   $a1 online resource (xvi, 360 pages) $cillustrations (some color)
311 08$a3-031-48237-9 
311 08$a3-031-48234-4 
320   $aIncludes bibliographical references and index.
327   $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.
330   $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. .
606   $aComputational intelligence
606   $aEducation$xData processing
606   $aSoftware engineering
606   $aRobotics
606   $aComputational Intelligence
606   $aComputers and Education
606   $aSoftware Engineering
606   $aRobotics
615  0$aComputational intelligence.
615  0$aEducation$xData processing.
615  0$aSoftware engineering.
615  0$aRobotics.
615 14$aComputational Intelligence.
615 24$aComputers and Education.
615 24$aSoftware Engineering.
615 24$aRobotics.
676   $a006.3
700   $aS?tuikys$b V$g(Vytautas)$01762714
701   $aBurbaite?$b Renata$01586158
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910799493703321
996   $aEvolution of STEM-Driven Computer Science Education$94463202
997   $aUNINA