This volume explores the rapidly advancing field of technology-supported knowledge assessment. Across academia, research on learning and instruction, AI-based analysis, psychology, and education, there is a pressing need for a comprehensive collection of foundations and methodologies related to knowledge. While the market offers books on individual and locally developed methods, a holistic overview is currently lacking. It aims to fill that gap, inspiring projects globally and benefiting knowledge-intensive developments in both digital and traditional learning environments. Understanding the state and processes of knowledge often poses a bottleneck in the quality of designs and implementations. This book addresses this challenge by focusing on mostly automated, easy-to-implement strategies, supporting the crucial task of understanding knowledge.
Part 1: Conceptual Perspectives
Chapter 1: On The Process, Use And
Methodological Challenges Of Assessing Knowledge.
Chapter 2: Framing
Knowledge As Conceptual Structure.
Chapter 3: Knowledge In The Era Of
Artificial Intelligence.
Chapter 4: A Framework For Data-Driven
Computer-Based Diagnostics Of Competencies And Capabilities Across Contexts.-
Part 2: Applied Perspectives.
Chapter 5: T-MITOCAR. An Epistemological
Approach To Assessing Artifacts Of Knowledge.
Chapter 6: Designing Effective
Technologies to Support Self-Regulated Strategies Development for Writing.-
Chapter 7: Sequential Pattern Mining On Cyber Ranges For A Computer-Based
Diagnostic Of Cybersecurity Skills.
Chapter 8: Tracking Competency
Development In Highly Interactive Digital Learning Environments.
Chapter 9:
Modeling Creativity In Education. Assessing Creativity In Students Scratch
Projects: A Study On Human-AI Collaboration For Creativity Assessment.-
Chapter 10: The Next Level In Personalized Learning: Adaptation Of
Educational Chatbots To Students Individual Learning.- Chapter 11:
Technology-Enhanced Feedback In K-12 Schools: Utilizing T-MITOCAR For
Knowledge Artifact And Feedback.- Chapter 12: Bridging The Gap Between Math
Formalism And Natural Language.
Pablo Pirnay-Dummer is Professor of Educational Psychology at Martin Luther University of Halle-Wittenberg, Germany. His research interests are the processes of language in learning, computer-linguistic methods for the psychological identification of knowledge in text, modelling and comparison of knowledge domains, expertise and complex problem-solving in processes of learning and instruction.
Dirk Ifenthaler is Professor and Chair of Learning, Design and Technology at the University of Mannheim, Germany and UNESCO Co-Chair on Data Science in Higher Education Learning and Teaching at Curtin University, Australia. Dirks research focuses on the intersection of cognitive psychology, educational technology, data analytics, and organizational learning.
