Linked e-resources
Details
Table of Contents
Intro
Preface
References
Acknowledgements
Contents
About the Editors
Part I: Analytics-Driven Perspectives
Chapter 1: Behavioral Patterns and Learner Interactions in Enterprise MOOCs
1 Introduction
2 The openSAP University
3 Sequential Analysis of Learning Behavior
4 Learner Behavior in Enterprise MOOCs
4.1 Sample, Data Collection, and Procedure
4.2 Results
4.2.1 Behavioral Patterns Over All Courses
4.2.2 Differences in Behavioral Patterns According to Topic Area
4.2.3 Typical Interaction Sequences
5 Discussion
Appendix: Detailed Findings for System Interactions
References
Chapter 2: The Smart MOOC Integrated with Intelligent Tutoring: A Case Study
1 Introduction
2 Method
2.1 Research Model
2.2 Participants
2.3 Data Collection Tools and Data Analysis
3 Results
3.1 Student Views on the Benefits of the Smart MOOC Integrated with Intelligent Tutoring Environment
3.2 Student Views on the Disliked Features of the Smart MOOC Integrated with Intelligent Tutoring Environment
3.3 Student Views on the Use of Smart MOOC Integrated with Intelligent Tutoring Environment Developed in Different Learning Contexts
3.4 Student Views on the Ease of Use of the Smart MOOC Integrated with Intelligent Tutoring Environment
3.5 Suggesting Situations of Students to Use the Smart MOOC Integrated with Intelligent Tutoring Environment to their Friends
3.6 Students' Views on the Aspects of the Smart MOOC Integrated with Intelligent Tutoring Environment that Are Open to Improvement
4 Conclusion and Discussion
References
Chapter 3: Incorporating Time in Dispositional Learning Analytics Models
1 Introduction
2 Temporality in Learning Analytics
2.1 The Role of Time in Learning
2.2 Seeking Balance
2.3 Research Objectives
3 Methods
3.1 Context and Setting
3.2 Participants
3.3 E-Tutorial Log Data
3.4 Disposition Data
3.5 Statistical Analyses
4 Results
4.1 Student Engagement Profiles by Clustering Log Data
4.2 Relevance of Clustering-Based Profiles for Course Performance
4.3 Profiles and Student Aptitudes as Dispositions
5 Discussion and Conclusions
References
Chapter 4: The GoalTrees Hierarchical Goal-Setting Intervention for Higher Education
1 Introduction
1.1 Hierarchical Goal Setting in a Digital Study Assistant
2 Three Recent Studies
2.1 Development History
2.2 Study 1: Comparative UI/UX Testing
2.2.1 Methods
2.2.2 Results
2.2.3 Amount of Interactions and Time on Task
2.3 System Usability Scale
2.3.1 Ranking
2.3.2 Limitations
2.3.3 Summary and Implications
2.4 Study 2: Effects of OCEAN Personality Traits
2.4.1 Methods
2.4.2 Results
2.4.3 Summary and Implications
Preface
References
Acknowledgements
Contents
About the Editors
Part I: Analytics-Driven Perspectives
Chapter 1: Behavioral Patterns and Learner Interactions in Enterprise MOOCs
1 Introduction
2 The openSAP University
3 Sequential Analysis of Learning Behavior
4 Learner Behavior in Enterprise MOOCs
4.1 Sample, Data Collection, and Procedure
4.2 Results
4.2.1 Behavioral Patterns Over All Courses
4.2.2 Differences in Behavioral Patterns According to Topic Area
4.2.3 Typical Interaction Sequences
5 Discussion
Appendix: Detailed Findings for System Interactions
References
Chapter 2: The Smart MOOC Integrated with Intelligent Tutoring: A Case Study
1 Introduction
2 Method
2.1 Research Model
2.2 Participants
2.3 Data Collection Tools and Data Analysis
3 Results
3.1 Student Views on the Benefits of the Smart MOOC Integrated with Intelligent Tutoring Environment
3.2 Student Views on the Disliked Features of the Smart MOOC Integrated with Intelligent Tutoring Environment
3.3 Student Views on the Use of Smart MOOC Integrated with Intelligent Tutoring Environment Developed in Different Learning Contexts
3.4 Student Views on the Ease of Use of the Smart MOOC Integrated with Intelligent Tutoring Environment
3.5 Suggesting Situations of Students to Use the Smart MOOC Integrated with Intelligent Tutoring Environment to their Friends
3.6 Students' Views on the Aspects of the Smart MOOC Integrated with Intelligent Tutoring Environment that Are Open to Improvement
4 Conclusion and Discussion
References
Chapter 3: Incorporating Time in Dispositional Learning Analytics Models
1 Introduction
2 Temporality in Learning Analytics
2.1 The Role of Time in Learning
2.2 Seeking Balance
2.3 Research Objectives
3 Methods
3.1 Context and Setting
3.2 Participants
3.3 E-Tutorial Log Data
3.4 Disposition Data
3.5 Statistical Analyses
4 Results
4.1 Student Engagement Profiles by Clustering Log Data
4.2 Relevance of Clustering-Based Profiles for Course Performance
4.3 Profiles and Student Aptitudes as Dispositions
5 Discussion and Conclusions
References
Chapter 4: The GoalTrees Hierarchical Goal-Setting Intervention for Higher Education
1 Introduction
1.1 Hierarchical Goal Setting in a Digital Study Assistant
2 Three Recent Studies
2.1 Development History
2.2 Study 1: Comparative UI/UX Testing
2.2.1 Methods
2.2.2 Results
2.2.3 Amount of Interactions and Time on Task
2.3 System Usability Scale
2.3.1 Ranking
2.3.2 Limitations
2.3.3 Summary and Implications
2.4 Study 2: Effects of OCEAN Personality Traits
2.4.1 Methods
2.4.2 Results
2.4.3 Summary and Implications