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Full Description
This book explores how data science, which involves preparing, analyzing, visualizing, and interpreting data, can revolutionize the field of education. The authors delve into how schools and universities can analyze data to improve teaching methods, enhance student learning, and design effective evaluations.
Learning Analytics: Shaping the Future of Education with Data Science examines how machine learning algorithms can analyze individual student performance data to tailor personalized adaptive learning paths, ensuring the best educational experience. Through real-world examples, this book discusses how valuable insights and opportunities can be gained through the application of data science in educational environments. The authors discuss the application of natural language processing (NLP) to analyze educational content, providing insights into language usage, comprehension levels, and improving the effectiveness of instructional materials and examines computer vision in classroom dynamics to measure student engagement. The book also exposes the reader to the crucial role of cybersecurity in safeguarding sensitive student and institutional information, ensuring a secure learning environment, and protecting against cyber threats. It also addresses the ethical considerations and privacy concerns associated with collecting, analyzing, and making decisions from educational data. Finally, it emphasizes the importance of responsible practices to protect the rights and well-being of students and educators.
The book is intended for engineers from computer science, government policy makers, institutions, and educational stakeholders. It shows how computer science, statistics, and data can personalize learning, improve educational tools, enhance classroom dynamics, secure academic records with blockchain, and ensure online safety.
Contents
Chapter 1 Use of virtual reality in learning environments and its impact on mental health Chapter 2 AI‑Driven Adaptive Learning: Architecture, Governance, and a Roadmap for Scalable Personalization Chapter 3 Enhancing AI Model Reliability: Mitigating Synthetic Data Risks with Hybrid Data and Explainable AI Chapter 4 Intelligent Educators: From Personalization to Educational Autonomy Chapter 5 An Augmented Reality-Enhanced Ecosystem for Literacy and Learning in Primary Education Chapter 6 Semantic Web and Complex Thinking: The Usefulness of Computational Tools for Achieving Professional Competencies in Health Chapter 7 Beyond GPA: Learning Analytics Reveals Plural Pathways to Academic Success Among Scholarship Recipients Chapter 8 Accessible Human Computer Interaction (HCI) for Inclusive Education: Designing Educational Tools for Diverse Learners Chapter 9 Emerging Technologies and Regenerative Pedagogies in the Era of Education 6.0 Chapter 10 Measuring Educational Initiatives through Student Engagement: A Data-Driven Evaluation Framework in Engineering Education Chapter 11 Teacher-in-the-loop Learning Analytics for LLM-Enhanced Intelligent Tutoring Systems Chapter 12 Multimodal Learning Analytics in Practice: Lessons from the 1st IFE Experiential Classroom Call Chapter 13 Algorithmic Bias and Human Computer Interaction in Educational Platforms: A Qualitative Approach from Substantive Equity Chapter 14 Digital Microcredentials in Latin America and the Caribbean: Ecosystem Maturity, Regulatory Frameworks, Blockchain Infrastructure, and Credential Analytics for Regional Governance
