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Description
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.
Table of Contents
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.
