Full Description
This book integrates foundational ideas from psychology, immersive digital learning environments supported by theories and methods of the learning sciences, particularly in pursuit of questions of cognition, behavior and emotion factors in digital learning experiences. New and emerging foundations of theory and analysis based on observation of digital traces are enhanced by data science, particularly machine learning, with extensions to deep learning, natural language processing and artificial intelligence brought into service to better understand higher-order thinking capacities such as self-regulation, collaborative problem-solving and social construction of knowledge. As a result, this edited volume presents a collection of indicators or measurements focusing on learning processes and related behavior, (meta-)cognition, emotion and motivation, as well as social processes. In addition, each section of the book includes an invited commentary from a related field, such as educational psychology, cognitive science, learning science, etc.
Contents
Introduction by the Editors.- Section on Indicators focusing on Behavior.- Modelling student behavioral engagement.- Measuring engagement with multimodal data.- Commentary from Learning Design or Learning Science.- Section on Indicators focusing on Cognition and Metacognition.- Measuring learning from text.- Learning strategies.- Metacognitive prompts and personalized scaffolding.- Commentary from Psychometrician or Cognitive Science.- Section on Indicators focusing on Emotion and Motivation.- Affect detection methods and techniques.- Emotions and ITS.- Emotion regulation in collaborative learning.- Measuring motivation from hypertext.- Commentary from Educational Psychology.- Section on Indicators focusing on Social Processes.- Modelling dynamics of social processes.- Linguistic analysis of student collaboration.- Group cohesion.- Commentary from Computational Social Science.- Concluding remarks and future directions by the Editors.
