Educational Data Analytics for Teachers and School Leaders

Preface
Contents
Chapter 1: Online and Blended Teaching and Learning Supported by Educational Data
1.1 Introduction and Scope
1.1.1 Scope
1.1.2 Chapter Learning Objectives
1.1.3 Introduction
1.2 Educational Data as a Key Success Factor for Online and Blended Teaching and Learning
1.2.1 Educational Data for Data-Driven Decision Making
1.2.2 Why Educational Data Is Important for Online and Blended Teaching and Learning?
1.2.3 How Educational Data Can Help Instructional Designers and e-Tutors of Online Courses?
1.2.4 How Educational Data Can Help School Teachers of Blended Courses?
1.2.5 The Learn2Analyze Educational Data Literacy Competence Framework
1.3 Data Is Everywhere (Educational Data Collection)
1.3.1 Posing Questions and Identifying Appropriate Educational Data
1.3.2 Matching Appropriate Educational Data with Data Sources
1.3.3 Combining Data from Different Educational Data Sources
1.4 Concluding Self-Assessed Assignment
1.4.1 Introduction
1.4.2 Step 1. Real Life Scenario
1.4.3 Step 2. Getting Familiar with the Assessment Rubric
1.4.3.1 Initial Educational Data Collection Plan
1.4.3.2 Rubric for Assessing the Educational Data Collection Plan
1.4.4 Step 3. Prepare Your Answer
1.4.5 Step 4. Review a Sample Solution
1.4.5.1 Exemplary Sample Solution
1.4.6 Step 5. Self-Evaluate Your Answer
References
Useful Video Resources
Further Reading
Chapter 2: Adding Value and Ethical Principles to Educational Data
2.1 Introduction and Scope
2.1.1 Scope
2.1.2 Chapter Learning Objectives
2.1.3 Introduction
2.2 Adding Value to Educational Datasets (Educational Data Management)
2.2.1 Making Data Tidy (Data Cleaning)
2.2.2 Data to Describe Data (Metadata)
2.2.3 The Significance of Data Curation
2.2.4 Storage Issues for Preserving Educational Data
2.3 Educational Data Ethics
2.3.1 Informed Consent
2.3.2 Sensitive Educational Data Protection
2.4 Concluding Self-Assessed Assignment
2.4.1 Introduction
2.4.2 Step 1. Real Life Scenario
2.4.3 Step 2. Getting Familiar with the Assessment Rubric
2.4.3.1 Initial Consent Form
Introduction
Privacy Policy
Information Collected on Our LMS:
Disclosure of Personal Information to Third Parties:
Receiving Promotional Materials:
Cookies
Links to Other Websites:
Terms of Use
For Informational Purposes Only
Copyright and Trademark Information
License for Your Use
External Links
2.4.3.2 Rubric for Assessing the Consent Form
2.4.4 Step 3. Prepare Your Answer
2.4.5 Step 4. Review a Sample Solution
2.4.5.1 Exemplary Sample Solution
2.4.6 Step 5. Self-Evaluate Your Answer
References
Useful Video Resources
Further Readings
Chapter 3: Learning Analytics
3.1 Introduction and Scope
3.1.1 Scope
3.1.2 Chapter Learning Objectives
3.1.3 Introduction
3.2 Using Learner-Generated Data and Learning Context for Extracting Learning Analytics
3.2.1 Definition and Objectives of Learning Analytics
3.2.2 Measurements as Indicators of Learners’ Current Learning States
3.2.3 Limitations and Data Quality Issues of Learners’ Data Measurements in Open and Blended Courses
3.2.4 Ethical Treatment of Learner-Generated Data and Measurements
3.3 Analyzing Data and Presenting Learning Analytics
3.3.1 Methods for Analyzing the Learner-Generated Data and the Measurements Over Them
3.3.2 Presentation Methods for Reporting on Learner Data Analytics
3.4 Interpreting Learning Analytics and Inferring Learning Changes
3.4.1 Making Sense of Learners’ Data Analytics and Analysis Results
3.4.2 Explaining the Data Analysis Results in an Educationally Meaningful Manner to Understand Learners and the Environment they Learn In
3.5 Concluding Self-Assessed Assignment
3.5.1 Introduction
3.5.2 Step 1. Real Life Scenario
3.5.3 Step 2. Getting Familiar with the Assessment Rubric
3.5.3.1 Initial Example DB
3.5.3.2 Rubric for Assessing the Example DB
3.5.4 Step 3. Prepare Your Answer
3.5.5 Step 4. Review a Sample Solution
3.5.5.1 Εxemplary Sample Solution
3.5.6 Step 5. Self-Evaluate Your Answer
References
Useful Video Resources
Further Readings
Chapter 4: Teaching Analytics
4.1 Introduction and Scope
4.1.1 Scope
4.1.2 Chapter Learning Objectives
4.1.3 Introduction
4.2 Data Sources for Supporting Teaching Analytics
4.2.1 Learning and Teaching
4.2.2 Design of Learning Environments
4.2.3 Learning Design
4.2.4 TPACK Model
4.3 Data Sources Within the Instructional Design Process
4.3.1 Broadening the Perspective for Data-Driven Education
4.3.2 Data Sources Within a Holistic Analytics Framework
4.3.3 Sources of Learner Data
4.3.4 Sources of Online Learning Data
4.4 Key Concepts of Data Quality and Limitations of Data Meaningfulness
4.4.1 Data Quality in Educational Contexts
4.4.2 Core Dimensions of Data Quality
4.4.3 Dimensions of Educational Data Quality
4.4.4 Data Quality Problems
4.5 Data Ethics and Privacy Principles for Teaching Analytics
4.5.1 Ethical and Privacy Challenges Associated with the Application of Educational Data Analytics
4.5.2 Privacy in the Digital World
4.5.3 Ethical Principles
4.6 Identify Issues of Authorship, Ownership, Data Access and Data-Sharing
4.6.1 Privacy Calculus
4.6.2 Educational Data Analytics Benefits
4.6.3 Data for Instructional Support
4.6.4 Data for Instructional Support
4.6.5 Data Privacy in Productive Systems
4.6.6 Case Study: Curtin Challenge I
4.6.7 Case Study: Curtin Challenge II
4.7 Applying and Communicating Educational Data and Analytics Findings
4.7.1 Adaptive Learning Technologies
4.7.2 Automated and Semi-Automated Interventions
4.7.3 Instructional Design Principles for Adaptivity
4.8 Methodologies for Improving Learning and Teaching Processes as Well as Curricula
4.8.1 Creating Interventions in Classroom Settings
4.8.2 Educational Design Research at a Glance
4.8.3 Designing Model-Based Learning Environments
4.9 Concluding Self-Assessed Assignment
4.9.1 Introduction
4.9.2 Step 1. Real Life Scenario
4.9.3 Step 2. Prepare Your Answer
4.9.4 Step 3. Exemplary Sample Solution
4.9.5 Step 4. Rubrics for Assessing Your Work
4.9.6 Step 5. Self-Evaluate Your Answer
References
Useful Video Resources
Further Readings
Appendix
Learn2 Analyse Educational Data Literacy Competence Framework