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Record Nr. |
UNINA9910799493703321 |
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Autore |
Stuikys Vytautas |
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Titolo |
Evolution of STEM-Driven Computer Science Education : The Perspective of Big Concepts |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing AG, , 2024 |
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©2024 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (368 pages) |
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Altri autori (Persone) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Contents -- Abbreviations -- 1 Context and Model for Writing This Book: An Idea of Big Concepts -- 1.1 Introduction -- 1.2 A Short Glance to Education Evolution -- 1.3 A Short Glance to Computing Evolution -- 1.4 A Short Glance to STEM Evolution -- 1.5 A Short Glance to Computational Thinking Skills -- 1.6 Context Model to Define Our Approach -- 1.7 Evolutionary Model for Change -- 1.8 The Topics This Book Addresses -- 1.9 Concluding Remarks -- References -- Part I Pedagogical Aspects of STEM-Driven CS Education Evolution: Integrated STEM-CS Skills Model, Personalisation Aspects and Collaborative Learning -- 2 Models for the Development and Assessment of Integrated STEM (ISTEM) Skills: A Case Study -- 2.1 Introduction -- 2.2 The Aim and Motivation -- 2.3 Research Tasks and Methodology -- 2.4 Related Work -- 2.5 Defining Context and Functionality for STEM-CS Skills -- 2.6 Defining the Structure of STEM-CS Skills Model -- 2.7 Analysis of the Interdependencies Among Different Skills -- 2.8 Feature-Based STEM-CS Skills Model (RQ3) -- 2.9 Analysis of Metrics and Defining Metrics Model for Skills Evaluation -- 2.10 Model for Evaluating and Describing of the ISTEM-CS Skills -- 2.11 Validation of the ISTEM-CS Skills Model Through Case Study (RQ6) -- 2.12 ISTEM-CS Skills and Their Metrics Generating Tool -- 2.13 Summarising Discussion and Evaluation -- 2.14 Conclusion -- Appendix -- References -- 3 Enforcing STEM-Driven CS Education Through Personalisation -- 3.1 Introduction -- 3.2 Related Work -- 3.3 |
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Requirements for Personalised STEM-Driven CS Learning and Research Questions -- 3.4 Basic Idea and Methodology -- 3.5 Background -- 3.6 A Framework for Implementing Personalised STEM-Driven CS Education -- 3.6.1 Structural Models of Personalised LOs -- 3.6.2 Personalised Processes and Activities Within the Framework. |
3.6.3 Tools and Approaches to Implement the Proposed Framework -- 3.7 Case Study -- 3.8 Discussion and Concluding Remarks -- References -- 4 Personal Generative Libraries for Personalised Learning: A Case Study -- 4.1 Introduction -- 4.2 Related Work -- 4.3 The Concept of the Personal Generative Library -- 4.4 Methodology and Background -- 4.5 Structure and Functionality of PGL -- 4.6 Integration of PGLs into the Framework of Personalised Learning -- 4.7 Case Study and Results -- 4.8 Discussion and Evaluation -- 4.9 Conclusion -- References -- 5 Enforcing STEM-Driven CS Education Through Collaborative Learning -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Basic Idea of the Approach and Methodology -- 5.4 The Concept 'Real-World Task' in STEM Research and Its Complexity -- 5.4.1 Complexity Issues of Real-World Tasks -- 5.4.2 Conceptual Model for Solving Real-World Tasks -- 5.5 Framework for STEM-Driven Contest-Based Collaborative Learning -- 5.6 Case Study -- 5.7 Discussion and Evaluation -- 5.8 Conclusion -- Appendix -- References -- Part II Internet of Things (IoT) and Data Science (DS) Concepts in K-12 STEM-Driven CS Education -- 6 Methodological Aspects of Educational Internet of Things -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Research Strategy, Aim, and Requirements -- 6.4 Motivation and Basic Idea -- 6.5 Background: Conceptual Modelling of IoT -- 6.6 A Framework for Introducing IoT into STEM-CS Education -- 6.7 Interpretation of IoT Architecture for STEM-Driven CS Education -- 6.8 Discussion on Proposed Methodology -- 6.9 Conclusion -- References -- 7 Multi-stage Prototyping for Introducing IoT Concepts: A Case Study -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Methodology: Implementation Aspects Through Modelling -- 7.3.1 A Multi-stage Model for Introducing IoT into STEM-Driven CS Education. |
7.3.2 A Framework for Solving Real-World Tasks Through IoT Prototyping -- 7.3.3 A Detailed Specification of IoT Prototype Design Processes -- 7.3.4 IoT Prototyping Task Solving Through Inquiry-Based and Design-Oriented Collaborative Learning -- 7.4 Extending Smart Learning Environment with Tools for IoT Prototyping -- 7.5 Case Study -- 7.6 Summarising Discussion and Evaluation -- 7.7 Conclusion -- References -- 8 Introducing Data Science Concepts into STEM-Driven Computer Science Education -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Motivation and Research Methodology -- 8.4 Conceptual Model for Introducing DS Concepts into K-12 -- 8.5 Implementation of the Methodology: A Three-Layered Framework -- 8.6 Development of the DS Model -- 8.7 Extending Smart Learning Environment -- 8.8 Modelling for Developing the Task Solution System -- 8.9 Development of the Assessment Model -- 8.10 A Case Study and Experiments -- 8.11 Summarising Discussion and Evaluation -- 8.12 Conclusion -- References -- Part III Introduction to Artificial Intelligence -- 9 A Vision for Introducing AI Topics: A Case Study -- 9.1 Introduction -- 9.2 Related Work -- 9.3 Background and AI Key Concepts -- 9.4 A Framework for Introducing AI Topics -- 9.5 Methodology for Implementing the Proposed Framework -- 9.6 Generic Architecture for Introducing AI Tools into SLE -- 9.7 Adopted Generic Scenario for Delivery of the AI Content -- 9.8 Summarising Discussion and Conclusion -- References -- 10 Speech Recognition Technology in K-12 STEM-Driven Computer Science Education -- 10.1 Introduction |
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-- 10.2 Related Work -- 10.3 Basic Idea with Motivating Scenario -- 10.4 Background -- 10.5 Research Methodology -- 10.6 Extending Smart Learning Environment for Speech Recognition Tasks -- 10.7 Case Study to Support Task 1 -- 10.8 Case Study to Support Task 3. |
10.9 Summarising Discussion and Conclusions -- Appendix 1 -- Appendix 2 -- Appendix 3 -- References -- 11 Introduction to Artificial Neural Networks and Machine Learning -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Operating Tasks and Methodology -- 11.4 Background: Basic Concepts and Models of ANNs (RQ2) -- 11.5 Motivating Example: A Binary Classification (RQ3) -- 11.6 Case Study 1: Implementation of Single-Layered Perceptron Model -- 11.7 Case Study 2: Implementation of Multi-Layered Perceptron Model -- 11.8 Summarising Discussion and Evaluation -- 11.9 Conclusion -- References -- 12 Overall Evaluation of This Book Concepts and Approaches -- 12.1 Aim and Structure of This Chapter -- 12.2 What Is the Contribution of This Book? -- 12.3 Difficulties and Drawbacks of the Proposed Approach -- 12.4 Rethinking of Discussed Approach -- 12.5 STEM-Driven Precision Education: A Vision Inspired by Concepts Discussed in This Book -- 12.6 Topics for Future Work -- References -- Index. |
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