Anmerkung: Intro -- Foreword -- Preface -- Contents -- 1 Machine Learning Paradigms -- References -- Learning Analytics with the Purpose to Measure Student Engagement, to Quantify the Learning Experience and to Facilitate Self-Regulation -- 2 Using a Multi Module Model for Learning Analytics to Predict Learners' Cognitive States and Provide Tailored Learning Pathways and Assessment -- 2.1 Introduction -- 2.2 Related Work -- 2.3 Multi Module Model and Logical Architecture of the System -- 2.4 Learners Clustering, Using the K-Means Algorithm, Supporting System's Modules -- 2.5 Evaluation and Discussion of Experimental Results -- 2.6 Ethics and Privacy for Learning Analytics -- 2.7 Conclusions and Future Work -- References -- 3 Analytics for Student Engagement -- 3.1 Effects of Student Engagement -- 3.2 Conceptualizing Student Engagement -- 3.3 Measuring Student Engagement -- 3.4 Analytics for Student Engagement -- 3.4.1 Early Alert Analytics -- 3.4.2 Dashboard Visualization Analytics -- 3.5 Dashboard Visualizations of Student Engagement -- 3.6 Comparative Reference Frame -- 3.7 Challenges and Potential Solutions for Analytics of Student Engagement: -- 3.7.1 Challenge 1: Connecting Engagement Analytics to Recommendations for Improvement -- 3.7.2 Potential Solutions: Using Diverse Metrics of Engagement to Improve Feedback Provided -- 3.7.3 Challenge 2: Quantifying Meaningful Engagement -- 3.7.4 Potential Solutions: Analytics Reflecting Quantity and Quality of Student Engagement -- 3.7.5 Challenge 3: Purposeful Engagement Reflection -- 3.7.6 Potential Solutions: Options for Purposeful Engagement Reflection -- 3.7.7 Challenge 4: Finding an Appropriate Reference Norm -- 3.7.8 Potential Solutions: Alternative Reference Frames -- 3.8 Conclusion -- References -- 4 Assessing Self-regulation, a New Topic in Learning Analytics: Process of Information Objectification. , 4.1 Introduction -- 4.2 Math Learning Process -- 4.3 Analyzing Empirical Evidence -- 4.3.1 Observations on a Learning Episode -- 4.3.2 Setting the Task -- 4.3.3 Students and Knowing Math -- 4.4 Math Meaningfulness and Three Modes of Manipulating the Blue Graph -- 4.4.1 The Adaptation Process: Dragging Points and Using Sliders -- 4.4.2 Typing the Parameters Values -- 4.4.3 Perceiving the 'a' Parameter and Its Properties -- 4.4.4 Typing Values Without Immediate Feedback -- 4.5 Discussion -- 4.5.1 Metacognitive Enactivism -- 4.6 As a Conclusion -- 4.6.1 Objectification as a Condition for Academic Knowing -- References -- Learning Analytics to Predict Student Performance -- 5 Learning Feedback Based on Dispositional Learning Analytics -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Educational Context -- 5.2.2 The Crucial Predictive Power of Cognitive Data -- 5.2.3 An Unexpected Source of Variation: National Cultural Values -- 5.2.4 LA, Formative Assessment, Assessment of Learning and Feedback Preferences -- 5.2.5 LA and Learning Emotions -- 5.3 The Current Study -- 5.3.1 Participants -- 5.3.2 E-tutorial Trace Data -- 5.3.3 Performance Data -- 5.3.4 Disposition Data -- 5.3.5 Analyses -- 5.4 Results -- 5.4.1 Performance -- 5.4.2 National Cultural Values -- 5.4.3 Cognitive Learning Processing Strategies -- 5.4.4 Metacognitive Learning Regulation Strategies -- 5.4.5 Attitudes and Beliefs Towards Learning Quantitative Methods -- 5.4.6 Epistemic Learning Emotions -- 5.4.7 Activity Learning Emotions -- 5.4.8 Adaptive Motivation and Engagement -- 5.4.9 Maladaptive Motivation and Engagement -- 5.5 Discussion and Conclusion -- References -- 6 The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 HOU Distance Learning Methodology and Data Description. , 6.4 Interview Based Survey Results -- 6.5 Machine Learning Techniques, Experiments and Results -- 6.5.1 Machine Learning Techniques, Experiments and Results -- 6.5.2 The Experiments -- 6.5.3 Results -- 6.5.4 Student Behavior Tool -- 6.6 Discussion -- 6.7 Conclusion -- Appendix -- References -- Learning Analytics Incorporated in Tools for Building Learning Materials and Educational Courses -- 7 An Architectural Perspective of Learning Analytics -- 7.1 Introduction -- 7.2 What is an Architectural Perspective? -- 7.3 Functional Viewpoints -- 7.3.1 Knowledge Discovery Functions -- 7.3.2 Analytical Functions -- 7.3.3 Predictive Functions -- 7.3.4 Generative Functions -- 7.4 Quality Attributes -- 7.5 Information Viewpoint -- 7.6 Architectural Patterns and Styles -- 7.6.1 Model-View-Control (MVC) -- 7.6.2 Publisher-Subscriber -- 7.6.3 Microservices -- 7.6.4 An Architecture for Learning Analytics -- 7.7 Discussion -- References -- 8 Multimodal Learning Analytics in a Laboratory Classroom -- 8.1 Introduction -- 8.2 Classroom Research -- 8.3 The Science of Learning Research Classroom -- 8.4 The Social Unit of Learning Project -- 8.5 Conceptualization(s) of Engagement -- 8.6 Multimodal Learning Analytics of Engagement in Classrooms -- 8.7 Observation Data -- 8.8 Features Selection, Extraction and Evaluation -- 8.8.1 Multimodal Behavioral Features -- 8.8.2 Feature Visualization -- 8.8.3 Feature Extraction Conclusions -- 8.9 Illustration of High Level Construct Based on Features Extracted -- 8.9.1 Attention to Teacher Speech -- 8.9.2 Teacher Attention -- 8.9.3 Student Concentration During Individual Task -- 8.9.4 Engagement During Pair and Group Work -- 8.10 Implications -- 8.11 Conclusion -- References -- 9 Dashboards for Computer-Supported Collaborative Learning -- 9.1 The Emergence of Learning Analytics and Dashboards -- 9.2 Collaborative Learning Theories. , 9.2.1 Group Cognition (GC) -- 9.2.2 Shared Mental Models (SMMs) -- 9.2.3 Situational Awareness (SA) -- 9.2.4 Socially Shared Regulation of Learning (SSRL) -- 9.3 Tools for CSCL -- 9.3.1 Group Awareness Tools (GATs) -- 9.3.2 Shared Mirroring Systems -- 9.3.3 Ambient Displays -- 9.4 Learning Dashboards for CSCL -- 9.5 How Can Collaborative Learning Dashboards Be Improved? -- 9.5.1 Principle 1: Adopt Iterative, User-Centred Design -- 9.5.2 Principle 2: Navigate the Theoretical Space -- 9.5.3 Principle 3: Visualize to Support Decision-Making -- References -- Learning Analytics as Tools to Support Learners and Educators in Synchronous and Asynchronous e-Learning -- 10 Learning Analytics in Distance and Mobile Learning for Designing Personalised Software -- 10.1 Introduction -- 10.2 Distance Learning -- 10.3 Mobile Learning and Mobile Learning Analytics -- 10.4 Personalised Learning Software -- 10.5 Data Collection -- 10.5.1 Modalities of Interaction in PCs -- 10.5.2 Modalities of Interaction in Smartphones -- 10.6 Multi-criteria Analysis -- 10.6.1 Combining Modalities of Interaction in HCI -- 10.6.2 Combining Modalities of Interaction in Smartphones -- 10.7 Conclusions -- References -- 11 Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Description of the Student Model -- 11.3.1 Analyzing Data That Have Been Gathered by the Implementation of ELaC -- 11.3.2 The Improved Student Model of ELaCv2 -- 11.4 Description of the Operation of the Student Model -- 11.4.1 Examples of Operation -- 11.5 Evaluation-Results -- 11.6 Conclusion -- References.