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Utilizing Learning Analytics to Support Study Success.

By: Ifenthaler, Dirk.
Contributor(s): Mah, Dana-Kristin | Yau, Jane Yin-Kim.
Material type: TextTextSeries: eBooks on Demand.Publisher: Cham : Springer, 2019Copyright date: ©2019Description: 1 online resource (341 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9783319647920.Subject(s): Educational technologyGenre/Form: Electronic books.Additional physical formats: Print version:: Utilizing Learning Analytics to Support Study SuccessDDC classification: 371.33 LOC classification: L1-991Online resources: Click here to view this ebook.
Contents:
Intro -- Preface -- Contents -- About the Editors -- About the Authors -- Part I: Theoretical and Technological Perspectives Linking Learning Analytics and Study Success -- Chapter 1: Educational Theories and Learning Analytics: From Data to Knowledge -- 1 Introduction -- 2 Understanding Learning -- 2.1 Evolution of Learning Theories -- 3 Role of Educational Theories in Learning Analytics -- 3.1 Research Methodology -- 3.2 Results and Discussion -- 3.2.1 Learning Theories and Learning Analytics Applications -- Self-Regulated Learning -- Motivation -- Social Constructivism -- Studies Using Specific Learning Concepts -- 3.2.2 Absence of Learning Theories -- 4 Conclusion and Suggestions for Future Research -- References -- Chapter 2: Utilising Learning Analytics for Study Success: Reflections on Current Empirical Findings -- 1 Introduction -- 2 Current Empirical Findings on Learning Analytics and Study Success -- 2.1 Research Methodology -- 2.2 Results of the Critical Reflection -- 2.2.1 Positive Evidence on the Use of Learning Analytics to Support Study Success -- 2.2.2 Insufficient Evidence on the Use of LA to Support Study Success -- 2.2.3 Link between Learning Analytics and Intervention Measures to Facilitate Study Success -- 3 Conclusion -- References -- Chapter 3: Supporting Stakeholders with Learning Analytics to Increase Study Success -- 1 Introduction -- 1.1 Learning Analytics -- 1.2 Benefits and Issues of Learning Analytics -- 1.3 Stakeholders of Learning Analytics -- 2 Evidence-Oriented Accompanying Research at Aalen University of Applied Sciences -- 2.1 Study Support Center -- 2.2 Study Design -- 2.2.1 Database and Data Protection -- 2.2.2 Research Questions -- 2.2.3 Research Method -- 2.2.4 Exemplary Analyses -- 3 Benefits of Learning Analytics at Aalen UAS for Different Stakeholders -- 3.1 Micro-Level -- 3.2 Meso-Level.
3.3 Macro-Level -- 3.4 Mega-Level -- 4 Summary and Conclusion -- References -- Chapter 4: Implementing Learning Analytics into Existing Higher Education Legacy Systems -- 1 Introduction -- 2 Concept and Implementation -- 2.1 General Concept -- 2.2 Data Privacy Issues -- 2.3 LeAP Core -- 2.4 Plug-In for Digital Learning Platform -- 2.5 Prompts -- 3 Pilot Study -- 3.1 Research Focus -- 3.2 Tracking Data (RQ1) -- 3.3 Prompts (RQ2) -- 3.4 Data Privacy (RQ3) -- 4 Discussion -- 4.1 Data Privacy -- 4.2 Impact of Prompts on the Learning Progress -- 5 Conclusion -- References -- Chapter 5: When Students Get Stuck: Adaptive Remote Labs as a Way to Support Students in Practical Engineering Education -- 1 Introduction -- 2 Dropout in Blended Learning -- 2.1 Motivation and Dropout -- 2.2 Cognitive Load and Dropout -- 2.3 User Behavior and Dropout -- 3 Study -- 3.1 Description of the Course -- 3.2 Methods and Instrumentation -- 3.3 Sample -- 4 Results -- 5 Discussion -- 5.1 Extraneous Cognitive Load: Practical Implications and Future Work -- 5.2 Error Streaks: Practical Implications and Future Work -- References -- Part II: Issues and Challenges for Implementing Learning Analytics -- Chapter 6: Learning Analytics Challenges to Overcome in Higher Education Institutions -- 1 Introduction -- 2 Related Work -- 3 Seven Main Categories for LA Implementations -- 3.1 Purpose and Gain -- 3.2 Representation and Actions -- 3.3 Data -- 3.4 IT Infrastructure -- 3.5 Development and Operation -- 3.6 Privacy -- 3.7 Ethics -- 4 Conclusion -- References -- Chapter 7: The LAPS Project: Using Machine Learning Techniques for Early Student Support -- 1 Introduction -- 2 Existing Work -- 3 The Laps Project -- 3.1 Data Basis -- 3.2 The LAPS Approach -- 3.3 Feasibility Study and Use in Consultations with Students -- 3.4 Privacy and Ethics.
3.5 Functionalities of the Tool to Support Students -- 3.6 Using LAPS for Quality Assurance -- 4 Discussion -- 5 Future Work -- References -- Chapter 8: Monitoring the Use of Learning Strategies in a Web-Based Pre-course in Mathematics -- 1 Introduction -- 2 Literature Review -- 3 Method -- 3.1 Pre-course Design -- 3.2 Data Collection -- 3.3 Data Analysis -- 3.3.1 Quantitative Data -- 3.3.2 Qualitative Data -- 3.4 Ethical Considerations -- 3.5 Limitations -- 4 Results -- 4.1 Prior Knowledge in Mathematics and Study Success in Engineering -- 4.2 Effects of Pre-course Participation on First Year Performance -- 4.3 Drivers of Successful Pre-course Participation -- 4.3.1 Attitude Towards Mathematics -- 4.3.2 Time Management and Organizational Strategies -- 4.3.3 Time on Task -- 4.3.4 Task Strategies -- 4.3.5 Additional Face-to-Face or e-Tutoring Support -- 4.3.6 Social Interaction, Help Seeking, and Peer Learning -- 4.3.7 Self-Evaluation and Self-Reflection -- 5 Summary and Discussion -- 5.1 Identification of Variables that Distinguish Between Successful and Less Successful Pre-course Participation of "At-Risk" Students -- 5.2 Contribution of Data Collected from Web-Based Pre-courses to the Field of Learning Analytics -- 5.3 Suggestions for the Support of "At-Risk" Students in the Transition Phase Between Secondary and Tertiary Education -- References -- Chapter 9: Issues and Challenges for Implementing Writing Analytics at Higher Education -- 1 Introduction -- 2 Background -- 2.1 Writing at Higher Education -- 2.2 Automated Analysis of Argumentation -- 2.3 Ethical Concerns Associated with Writing Analytics -- 3 Study -- 3.1 One-to-One Interviews -- 3.2 Focus Group -- 3.3 Results -- 3.3.1 One-to-One Interviews -- 3.3.2 Focus Group -- Theme 1: Belief -- Theme 2: Power and Politics -- Theme 3: Problems -- 4 Conclusion -- References.
Chapter 10: Digital Applications as Smart Solutions for Learning and Teaching at Higher Education Institutions -- 1 Introduction -- 2 Smart Solutions in Higher Education -- 2.1 Definition and Characteristics of Smart (Learning) Environments -- 2.2 Mobile Learning and Learning Analytics -- 2.2.1 Mobile Learning Definition and Features -- 2.2.2 Learning Analytics Definition and Features -- 2.3 Interim Conclusion (and Research Gap) -- 3 Project Mobile Learning Analytics -- 3.1 MyLA App -- 3.2 MyLA Dashboard -- 3.3 First Findings and Outlook -- 4 Long-Term Success of Smart Solutions -- 4.1 Opportunities for Stakeholders -- 4.2 Challenges for Stakeholders -- 5 Approaches for Implementation -- 5.1 Technology-Enhanced Learning Complex -- 5.2 The Rapid Outcome Mapping Approach -- 6 Conclusion and Outlook -- References -- Chapter 11: Learning Analytics to Support Teachers' Assessment of Problem Solving: A Novel Application for Machine Learning and Graph Algorithms -- 1 Introduction -- 2 Background -- 2.1 Developing Knowledge Structures Through Ill-Structured Problem Solving -- 2.2 Assessment in Ill-Structured Problem Solving -- 3 Using Graph Comparison Methods for Assessment -- 3.1 An Intuitive Introduction to Graph Comparison -- 3.2 Graph Edit Distance (GED) -- 3.3 Graph Kernels -- 3.4 Graph Embeddings -- 4 Implementation -- 4.1 Influence of Previous Implementations -- 4.2 Methods Selected for Each Form of Graph Comparison -- 4.3 Verification -- 5 Discussion -- 5.1 Context of the Chapter -- 5.2 New Insights -- 5.3 Future Work -- References -- Chapter 12: Utilizing Learning Analytics in Small Institutions: A Study of Performance of Adult Learners in Online Classes -- 1 Introduction -- 1.1 Learning Analytics and Data Mining -- 1.2 Online Learning -- 1.3 Adult Students -- 1.4 Retention Issues -- 2 Methodology -- 2.1 Setting.
2.1.1 Online Courses at the University -- 2.2 Data Sources -- 2.3 Data Preparation -- 3 Results -- 4 Discussion -- 4.1 Implications -- 4.2 Limitations -- 5 Conclusions -- References -- Part III: Learning Analytics Case Studies: Practices and Evidence -- Chapter 13: Empowering Teachers to Personalize Learning Support -- 1 Introduction -- 1.1 Students' Success and Teachers' Roles -- 1.2 The Contexts of Teaching and the Learning Analytics Needs of Teachers -- 2 The Student Relationship Engagement System (SRES) -- 3 Institutional Case Studies -- 3.1 Methodology -- 3.2 Case Study 1: The University of Sydney -- 3.3 Case Study 2: The University of Melbourne -- 3.4 Case Study 3: The University of New South Wales Sydney -- 4 Discussion -- 4.1 Empowering Teachers to Personalize Support for Student Success -- 4.2 Implications for Practice -- 4.2.1 Learning Analytics Needs to Address Actual Needs -- 4.2.2 Start Small but Provide for Growth -- 4.2.3 Foster Communities -- 4.3 Conclusion and Future Directions -- References -- Chapter 14: Predicting Success, Preventing Failure -- 1 Introduction -- 2 Research Background -- 2.1 Challenges in Peru's English Language Education -- 2.2 Enhancing English Instruction in Developing Countries Using Online Courses -- 2.3 Learning Analytics for Predicting Success in Online Courses -- 2.4 Self-Determination Theory -- 3 The Study -- 3.1 Research Variables -- 3.1.1 Independent Variables -- 3.1.2 Dependent Variables -- 3.2 Research Context -- 3.3 Sample Description -- 3.4 Method and Procedure -- 3.5 Statistical Analyses -- 4 Findings -- 5 Discussion -- References -- Chapter 15: Using Learning Analytics to Examine Relationships Between Learners' Usage Data with Their Profiles and Perceptions: A Case Study of a MOOC Designed for Working Professionals -- 1 Introduction -- 1.1 Use of Learning Analytics in MOOCs.
1.2 Learner Behavioral Patterns in MOOCs.
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
L1-991 (Browse shelf) https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=5639430 Available EBC5639430

Intro -- Preface -- Contents -- About the Editors -- About the Authors -- Part I: Theoretical and Technological Perspectives Linking Learning Analytics and Study Success -- Chapter 1: Educational Theories and Learning Analytics: From Data to Knowledge -- 1 Introduction -- 2 Understanding Learning -- 2.1 Evolution of Learning Theories -- 3 Role of Educational Theories in Learning Analytics -- 3.1 Research Methodology -- 3.2 Results and Discussion -- 3.2.1 Learning Theories and Learning Analytics Applications -- Self-Regulated Learning -- Motivation -- Social Constructivism -- Studies Using Specific Learning Concepts -- 3.2.2 Absence of Learning Theories -- 4 Conclusion and Suggestions for Future Research -- References -- Chapter 2: Utilising Learning Analytics for Study Success: Reflections on Current Empirical Findings -- 1 Introduction -- 2 Current Empirical Findings on Learning Analytics and Study Success -- 2.1 Research Methodology -- 2.2 Results of the Critical Reflection -- 2.2.1 Positive Evidence on the Use of Learning Analytics to Support Study Success -- 2.2.2 Insufficient Evidence on the Use of LA to Support Study Success -- 2.2.3 Link between Learning Analytics and Intervention Measures to Facilitate Study Success -- 3 Conclusion -- References -- Chapter 3: Supporting Stakeholders with Learning Analytics to Increase Study Success -- 1 Introduction -- 1.1 Learning Analytics -- 1.2 Benefits and Issues of Learning Analytics -- 1.3 Stakeholders of Learning Analytics -- 2 Evidence-Oriented Accompanying Research at Aalen University of Applied Sciences -- 2.1 Study Support Center -- 2.2 Study Design -- 2.2.1 Database and Data Protection -- 2.2.2 Research Questions -- 2.2.3 Research Method -- 2.2.4 Exemplary Analyses -- 3 Benefits of Learning Analytics at Aalen UAS for Different Stakeholders -- 3.1 Micro-Level -- 3.2 Meso-Level.

3.3 Macro-Level -- 3.4 Mega-Level -- 4 Summary and Conclusion -- References -- Chapter 4: Implementing Learning Analytics into Existing Higher Education Legacy Systems -- 1 Introduction -- 2 Concept and Implementation -- 2.1 General Concept -- 2.2 Data Privacy Issues -- 2.3 LeAP Core -- 2.4 Plug-In for Digital Learning Platform -- 2.5 Prompts -- 3 Pilot Study -- 3.1 Research Focus -- 3.2 Tracking Data (RQ1) -- 3.3 Prompts (RQ2) -- 3.4 Data Privacy (RQ3) -- 4 Discussion -- 4.1 Data Privacy -- 4.2 Impact of Prompts on the Learning Progress -- 5 Conclusion -- References -- Chapter 5: When Students Get Stuck: Adaptive Remote Labs as a Way to Support Students in Practical Engineering Education -- 1 Introduction -- 2 Dropout in Blended Learning -- 2.1 Motivation and Dropout -- 2.2 Cognitive Load and Dropout -- 2.3 User Behavior and Dropout -- 3 Study -- 3.1 Description of the Course -- 3.2 Methods and Instrumentation -- 3.3 Sample -- 4 Results -- 5 Discussion -- 5.1 Extraneous Cognitive Load: Practical Implications and Future Work -- 5.2 Error Streaks: Practical Implications and Future Work -- References -- Part II: Issues and Challenges for Implementing Learning Analytics -- Chapter 6: Learning Analytics Challenges to Overcome in Higher Education Institutions -- 1 Introduction -- 2 Related Work -- 3 Seven Main Categories for LA Implementations -- 3.1 Purpose and Gain -- 3.2 Representation and Actions -- 3.3 Data -- 3.4 IT Infrastructure -- 3.5 Development and Operation -- 3.6 Privacy -- 3.7 Ethics -- 4 Conclusion -- References -- Chapter 7: The LAPS Project: Using Machine Learning Techniques for Early Student Support -- 1 Introduction -- 2 Existing Work -- 3 The Laps Project -- 3.1 Data Basis -- 3.2 The LAPS Approach -- 3.3 Feasibility Study and Use in Consultations with Students -- 3.4 Privacy and Ethics.

3.5 Functionalities of the Tool to Support Students -- 3.6 Using LAPS for Quality Assurance -- 4 Discussion -- 5 Future Work -- References -- Chapter 8: Monitoring the Use of Learning Strategies in a Web-Based Pre-course in Mathematics -- 1 Introduction -- 2 Literature Review -- 3 Method -- 3.1 Pre-course Design -- 3.2 Data Collection -- 3.3 Data Analysis -- 3.3.1 Quantitative Data -- 3.3.2 Qualitative Data -- 3.4 Ethical Considerations -- 3.5 Limitations -- 4 Results -- 4.1 Prior Knowledge in Mathematics and Study Success in Engineering -- 4.2 Effects of Pre-course Participation on First Year Performance -- 4.3 Drivers of Successful Pre-course Participation -- 4.3.1 Attitude Towards Mathematics -- 4.3.2 Time Management and Organizational Strategies -- 4.3.3 Time on Task -- 4.3.4 Task Strategies -- 4.3.5 Additional Face-to-Face or e-Tutoring Support -- 4.3.6 Social Interaction, Help Seeking, and Peer Learning -- 4.3.7 Self-Evaluation and Self-Reflection -- 5 Summary and Discussion -- 5.1 Identification of Variables that Distinguish Between Successful and Less Successful Pre-course Participation of "At-Risk" Students -- 5.2 Contribution of Data Collected from Web-Based Pre-courses to the Field of Learning Analytics -- 5.3 Suggestions for the Support of "At-Risk" Students in the Transition Phase Between Secondary and Tertiary Education -- References -- Chapter 9: Issues and Challenges for Implementing Writing Analytics at Higher Education -- 1 Introduction -- 2 Background -- 2.1 Writing at Higher Education -- 2.2 Automated Analysis of Argumentation -- 2.3 Ethical Concerns Associated with Writing Analytics -- 3 Study -- 3.1 One-to-One Interviews -- 3.2 Focus Group -- 3.3 Results -- 3.3.1 One-to-One Interviews -- 3.3.2 Focus Group -- Theme 1: Belief -- Theme 2: Power and Politics -- Theme 3: Problems -- 4 Conclusion -- References.

Chapter 10: Digital Applications as Smart Solutions for Learning and Teaching at Higher Education Institutions -- 1 Introduction -- 2 Smart Solutions in Higher Education -- 2.1 Definition and Characteristics of Smart (Learning) Environments -- 2.2 Mobile Learning and Learning Analytics -- 2.2.1 Mobile Learning Definition and Features -- 2.2.2 Learning Analytics Definition and Features -- 2.3 Interim Conclusion (and Research Gap) -- 3 Project Mobile Learning Analytics -- 3.1 MyLA App -- 3.2 MyLA Dashboard -- 3.3 First Findings and Outlook -- 4 Long-Term Success of Smart Solutions -- 4.1 Opportunities for Stakeholders -- 4.2 Challenges for Stakeholders -- 5 Approaches for Implementation -- 5.1 Technology-Enhanced Learning Complex -- 5.2 The Rapid Outcome Mapping Approach -- 6 Conclusion and Outlook -- References -- Chapter 11: Learning Analytics to Support Teachers' Assessment of Problem Solving: A Novel Application for Machine Learning and Graph Algorithms -- 1 Introduction -- 2 Background -- 2.1 Developing Knowledge Structures Through Ill-Structured Problem Solving -- 2.2 Assessment in Ill-Structured Problem Solving -- 3 Using Graph Comparison Methods for Assessment -- 3.1 An Intuitive Introduction to Graph Comparison -- 3.2 Graph Edit Distance (GED) -- 3.3 Graph Kernels -- 3.4 Graph Embeddings -- 4 Implementation -- 4.1 Influence of Previous Implementations -- 4.2 Methods Selected for Each Form of Graph Comparison -- 4.3 Verification -- 5 Discussion -- 5.1 Context of the Chapter -- 5.2 New Insights -- 5.3 Future Work -- References -- Chapter 12: Utilizing Learning Analytics in Small Institutions: A Study of Performance of Adult Learners in Online Classes -- 1 Introduction -- 1.1 Learning Analytics and Data Mining -- 1.2 Online Learning -- 1.3 Adult Students -- 1.4 Retention Issues -- 2 Methodology -- 2.1 Setting.

2.1.1 Online Courses at the University -- 2.2 Data Sources -- 2.3 Data Preparation -- 3 Results -- 4 Discussion -- 4.1 Implications -- 4.2 Limitations -- 5 Conclusions -- References -- Part III: Learning Analytics Case Studies: Practices and Evidence -- Chapter 13: Empowering Teachers to Personalize Learning Support -- 1 Introduction -- 1.1 Students' Success and Teachers' Roles -- 1.2 The Contexts of Teaching and the Learning Analytics Needs of Teachers -- 2 The Student Relationship Engagement System (SRES) -- 3 Institutional Case Studies -- 3.1 Methodology -- 3.2 Case Study 1: The University of Sydney -- 3.3 Case Study 2: The University of Melbourne -- 3.4 Case Study 3: The University of New South Wales Sydney -- 4 Discussion -- 4.1 Empowering Teachers to Personalize Support for Student Success -- 4.2 Implications for Practice -- 4.2.1 Learning Analytics Needs to Address Actual Needs -- 4.2.2 Start Small but Provide for Growth -- 4.2.3 Foster Communities -- 4.3 Conclusion and Future Directions -- References -- Chapter 14: Predicting Success, Preventing Failure -- 1 Introduction -- 2 Research Background -- 2.1 Challenges in Peru's English Language Education -- 2.2 Enhancing English Instruction in Developing Countries Using Online Courses -- 2.3 Learning Analytics for Predicting Success in Online Courses -- 2.4 Self-Determination Theory -- 3 The Study -- 3.1 Research Variables -- 3.1.1 Independent Variables -- 3.1.2 Dependent Variables -- 3.2 Research Context -- 3.3 Sample Description -- 3.4 Method and Procedure -- 3.5 Statistical Analyses -- 4 Findings -- 5 Discussion -- References -- Chapter 15: Using Learning Analytics to Examine Relationships Between Learners' Usage Data with Their Profiles and Perceptions: A Case Study of a MOOC Designed for Working Professionals -- 1 Introduction -- 1.1 Use of Learning Analytics in MOOCs.

1.2 Learner Behavioral Patterns in MOOCs.

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