Manage Your Own Learning Analytics: Implement a Rasch Modelling Approach

Description

This book sheds light on the practice of learning analytics, illuminating how others approach their data analysis. At the beginning of the book, a ‘prescriptive learning analytics planning model’ gives straightforward instructions for people to follow. This book is organized into ten chapters, falling into four topical sections: Managing Learning Analytics (overview, instructional systems design (ISD), instructional design, and planning data analysis); Cognitive Performance Measurement Practices (classical test theory (CTT), Rasch measurement theory (RMT), Item response theory(IRT), Rasch Modeling Tools (research design, setting methodology); and Case Studies (corporate training settings, healthcare industry, and educational courseware design). This book is an important reference for: educational research community and instructional systems designers; corporate training developers; postgraduate course developers; and doctoral students. Preface Contents Editor and Contributors Privacy-Driven Learning Analytics Introduction Analytics and Privacy in Educational Research Learning Analytics Data and Associated Risks Policies and Frameworks Technical Solutions Stage 1—Risk Assessment and Privacy Measuring Re-Identification Risk Examples of Re-Identification Risk Mitigation Techniques Stage 2—Towards Provable Privacy Provable Privacy Enhancing Mechanisms Privacy-Driven Learning Analytics Conclusion References Using Log Stream Data and Item Response Theory to Understand Proficiency in Collaboration Assessing 21st Century Skills Method Overview Develop Tasks that Capture Log Stream Data Turning Log Stream Data into Measurement Items Results Fit of Items to the Rasch Model Wright Map Interpreting the Assessment Data Moving from a Wright Map to Understanding Skill Development A Broader Sense of the Skill Conclusion References Introductory Analysis of the Rasch Model Rasch Model Overview Tests Parameter Estimation Fit Statistics χ2 Statistic Outfit and Infit Significance and Sample Size Conclusion Appendix Dichotomous Model as a Function of Outcome, x R Code for Toy Example References Data Evaluation of Happiness Scale Online Study: A Rasch Measurement Analysis Introduction Background Literature Review Research Design and Methodology Research Questions Results Data Preparation Part: II Data Review Part: III Data Review Data Analysis Correlation Study Research Question-1 Research Question-2 Research Question-3 Research Question-4 Research Question-5 Research Question-6 Research Question-7 Research Question-8 Research Question-9 Research Question-10 Category Probability Curves Discussion Conclusions Appendix—Research Survey Research Questionnaire Addressed to Young Students of Secondary School/Technical School Part: II Survey Part III: The Scale of Experiencing Happiness (SDS) A Research Questionnaire for Young People References Social Media Analytics, Learning Analytics and Healthcare Industry: Risky Drinking Historical Overview The Intersection of Social Media Analytics and Learning Analytics Health and Human Services Context Case Study Healthcare Industry: Risky Drinking Introduction of the Case Organisation Overview of the Project The Scoping Literature Review Literature Search and Article Review Themes Literature Review Concluding Comments Population Surveys Data Collection Online Surveys Data Analysis Survey Findings General Population Survey Worker Survey Search Terms Talkwalker Implementation The Initial Topic Query Data Cleaning Final Topic Query Social Media Analytics Conclusion, Limitations and Directions for Future Research Double Meanings in the English Language Tweeting Style Sentiment Project Recommendations Summary References The Adoption of the Rasch Measurement Approach as a Learning Analytics Tool in Higher Education Introduction Learning Analytics Research Methodology Results Contextualised Analytics Personalised Learning Analytics Assessment Analytics Conclusion References Rasch Model Case Study: Investigating Cognitive Performances of School Children's Museum Learning Experiences Introduction Study Background Cognitive Styles Wholist-Analytic Dimension Verbal-Imagery Dimension Individual Cognitive Preferences Construct Multimedia Learning Museum Exhibit Representations Research Implementation Rasch Analysis as the Analytic Learning Tool Data Analysis Variable Map Fit Map Item Estimates (Thresholds) and Case Estimates Item Analysis Result Results and Findings The Overall Performances According to Treatment Group Single Cognitive Style Dimension (SCSD) Performances Combine Cognitive Style Dimension (CCSD) Performances The Interactive Effects Conclusion References Exploring a Framework for Constructivist Design with the eLearning Space eLearning Definition: Purposed for this Chapter Constructivist Instructional Design Knowledge Construction and Online Learning Design Justification for the Study Aim of the Study Methodology: Exploratory Sequential Design Limitations of the Study Overview of the Framework Learning Design Element 1: Social Learning Presence Learning Design Element 2: Social Learning Interaction Learning Design Element 3: Knowledge-Sharing Space Learning Design Element 4: Meta-Cognitive Load Learning Design Element 5: Knowledge Co-construction Proposed Framework for Construction of Knowledge Within eLearning Main Contribution of the Study References Industry 4.0: Learning Analytics Using Artificial Intelligence and Advanced Industry Applications Introduction Industry 4.0 Artificial Intelligence Education 4.0 Learning Analytics SWOT Analysis of Industry 4.0 and the Impact on Learning Analytics Conclusion References Machine Learning for Learning Analytics for Meta-cognitive Support Introduction Learning Analytics Stands on the Shoulders of Big Data, Deep and Machine Learning Learning Analytics: Evolutionary Step Forward? Impact and Privacy of Learning Analytics Learning Analytics for Personalizing Education Ingredients for Learning Analytics Learning Paradigms Learning Analytics; Analogy to Medical Optimization Meta-cognitive Representations for Learning Analytics Recursive Analytics: Coping with NP Completeness Mapping for Meta-cognitive Imagination Toward Didactic Topologies for Analytics References