000			04101cam a2200373 u 4500
001			10302013
003			CaAEU
005			20240319104119.0
006			m o d
007			cr cnu||||||||
008			230617s2023 xx a ob 001 0 eng d
020			_z9783031309915
024	7		_a10.1007/978-3-031-30992-2 _2doi
035			_aon1382693953
039			_aexclude
040			_aEBLCP _beng _cEBLCP _dYDX _dN$T _dGW5XE _dOCLCF _dSFB _dAEU
050		4	_aLB1028.3
245	0	0	_aUnobtrusive observations of learning in digital environments _bexamining behavior, cognition, emotion, metacognition and social processes using learning analytics
260			_aCham : _bSpringer International Publishing AG, _c2023.
300			_ax, 244 pages : _billustrations (some color)
490	1		_aAdvances in Analytics for Learning and Teaching Series
504			_aIncludes bibliographical references and index.
505	0		_aLearning Processes — Unobtrusive Observations of Learning Processes — Section Overview — A Review of Measurements and Techniques to Study Emotion Dynamics in Learning — The Features of Emotion Dynamics — Emotional Variability — Emotional Instability — Emotional Inertia — Emotional Cross-lags — Emotional Patterns — The Measurements of Emotion Dynamics — Experience Sampling Method — Emote-Aloud — Facial Expressions — Vocal Expressions — Language and Discourse — Physiological Sensors — The Techniques for Analyzing Emotion Dynamics — Conventional Statistical Methods — Entropy Analysis — Growth Curve Modeling — Time Series Analysis — Network Analysis — Recurrence Quantification Analysis — Sequential Pattern Mining — The Challenges of Studying Emotion Dynamics in Learning — Deciding What to Measure About Emotion Dynamics — Deciding How to Analyze Emotion Dynamics — Addressing Individual and Developmental Differences — Differentiating Between Short-Term and Long-Term Emotion Dynamics — Concluding Remarks and Directions for Future Research — References — Applying Log Data Analytics to Measure Problem Solving in Simulation-Based Learning Environments — Introduction — Background — Methods — Experiment — Experiment — Log Data Processing — Results — Problem-Solving Outcomes as Measured by Solution Quality — Problem-Solving Processes as Captured by Features Extracted from Log Data — Pause as a Generalizable Indicator of Deliberate Problem Solving — How Log Data-Based Features Were Associated with Specific Problem-Solving Practices — Discussion — Limitations.
520			_aThis 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.
650		0	_aInternet in education _xPsychological aspects.
650		0	_aWeb-based instruction _xPsychological aspects.
700	1		_aKovanovic, Vitomir.
700	1		_aAzevedo, Roger, _d1966-
700	1		_aGibson, David C.
700	1		_alfenthaler, Dirk.
830		0	_aAdvances in analytics for learning and teaching.
942			_2lcc
999			_c3843 _d3843
041			_aEnglish