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BL
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| Record Number
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865695
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| Main Entry
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Štuikys, Vytautas
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| Title & Author
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Smart STEM-driven computer science education : : theory, methodology and robot-based practices /\ Vytautas Štuikys, Renata Burbaitė.
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| Publication Statement
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Cham :: Springer,, 2018.
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| Page. NO
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1 online resource (xvii, 368 pages)
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| ISBN
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3319784854
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: 9783319784854
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3319784846
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9783319784847
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| Contents
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Intro; Preface; Acknowledgments; Contents; Part I: Introductory Part: Motivation, Challenges, and Conceptual Vision of STEM-Driven CS Education Based on Robotics; Chapter 1: Challenges of STEM-Driven Computer Science (CS) Education; 1.1 Introduction; 1.2 Motivation of Our Approach; 1.3 Smart Education and STEM; 1.4 Ten Top Challenges in STEM Education; 1.5 CS Teaching Challenges Without STEM Context; 1.6 Challenges of STEM-Driven CS Education; 1.7 The Bookś Objectives and Research Agenda; 1.8 The Topics This Book Addresses; 1.9 Summary and Concluding Remarks; References.
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2.6 STEM-Driven Learning Processes as a Problem Domain2.7 Summary, Discussion and Overall Evaluation; 2.8 Conclusion; References; Chapter 3: Smart Devices and Educational Robotics as Technology for STEM Knowledge; 3.1 Introduction; 3.2 Related Work; 3.3 Introducing Robotics in STEM-Driven CS Education; 3.4 Educational Robot Generic Architecture; 3.5 Conceptual Model of STEM-Driven Environment; 3.6 Discussion and Conclusion; References; Part II: Methodological and Theoretical Background of Approaches to Implement the Proposed Vision.
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4.6 Two Approaches of Dealing with Variability in STEM4.7 Summary, Evaluation and Extended Discussion; 4.8 Conclusion; References; Chapter 5: Theoretical Background to Implement STEM-Driven Approaches; 5.1 Introduction; 5.2 Motivation and Methodology of Describing the Background; 5.3 Related Work; 5.4 Background of Feature-Based Modelling; 5.4.1 A Vision for Researching STEM-Driven CS Education; 5.4.2 Basics of Feature Modelling; 5.4.3 Formal Definition of Features and Constraints; 5.4.4 Static and Dynamic Feature Models; 5.4.5 Mechanisms to Support Dynamicity for STEM.
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Chapter 2: A Vision for Introducing STEM into CS Education at School2.1 Introduction; 2.2 Related Work; 2.2.1 STEM-Based Education Challenges; 2.2.2 The Role of CS in STEM-Oriented Education; 2.2.3 The Role of Smart Devices and Educational Robotics in STEM-Driven CS Education; 2.2.4 The Role of Context in Analysis and Design of Educational Systems; 2.3 A General Description of Our Approach; 2.3.1 A Conceptual Model of STEM-Driven CS Education; 2.4 A Framework to Implement the Proposed Conceptual Model; 2.5 Basis for Implementing Our Approach: A Process-Based Vision.
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Chapter 4: A Methodological Background for STEM-Driven Reuse-Enhanced CS Education4.1 Introduction; 4.2 Related Work; 4.2.1 Variability Research in SWE; 4.2.2 Variability in Learning; 4.2.2.1 Feature-Based Variability in Learning; 4.2.2.2 Social Variability, Inclusive Teaching and STEM; 4.3 Explicit Representation of Variability: A Motivating Example; 4.3.1 Capabilities of Feature Diagrams in Learning Object Domain; 4.3.2 Limitations of Feature Diagrams in Learning Object Domain; 4.4 A Framework to Implement Learning Variability in STEM Paradigm; 4.5 Motivation of STEM-Driven Research Topics.
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| Abstract
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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.
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| Subject
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Computer science-- Study and teaching.
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Artificial intelligence.
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Computer science-- Study and teaching.
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COMPUTERS-- Computer Literacy.
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COMPUTERS-- Computer Science.
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COMPUTERS-- Data Processing.
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COMPUTERS-- Hardware-- General.
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COMPUTERS-- Information Technology.
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COMPUTERS-- Machine Theory.
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COMPUTERS-- Reference.
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Educational equipment technology, computer-aided learning (Calif.)
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| Subject
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Expert systems-- knowledge-based systems.
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| Dewey Classification
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004
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| LC Classification
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QA76.27
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| Added Entry
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Burbaitė, Renata
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