Machine learning paradigms : advances in learning analytics /: advances in learning analytics. ([2020])

• Record Type: Book
• Further Information: Note: Editors, Maria Virvou, Efthimios Alepis, George A. Tsihrintzis and Lakhmi C. Jain.
• Editors: Virvou, Maria; Alepis, Efthimios; Tsihrintzis, George A; Jain, L. C
• Contents: 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 Analytics2.7 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 Provided3.7.3 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 Objectification4.1 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 Enactivism4.6 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
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2020
• Extent: 1 online resource, illustrations
• Subjects: 371.334; Computer-assisted instruction; Learning; Educational technology; Machine learning; EDUCATION / Administration / General; EDUCATION / Organizations & Institutions; Electronic books; Electronic books
• Languages: English
• ISBNs: 9783030137434; 3030137430
• Related ISBNs: 9783030137427; 3030137422
• Notes: Note: Includes bibliographical references.; Note: Online resource; title from PDF title page (EBSCO, viewed March 20, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.