Evolution of STEM-Driven Computer Science Education: The Perspective of Big Concepts

✍ Scribed by Vytautas Štuikys, Renata Burbaitė

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 368
• Category: Library

✦ Synopsis

The 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-driven CS education strategies, curriculum development, and new ideas for research.

✦ Table of Contents

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 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
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