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Table of Contents
Intro; Contents; Learning Any Time, Anywhere: Big Educational Data from Smart Devices; 1 Introduction; 2 Mobile Practice of Course Content; 2.1 Smart Device Mobile Applications; 2.2 User Interface; 2.3 Algorithm Self-contained Within the Smart Device; 3 Data Messaging System; 3.1 Questions for Business Analytics; 3.2 Questions for Learning Science; 3.3 Data Fields and JSON Schema for the Messaging System; 3.4 JSON Schema; 3.5 Online and Offline Modes; 3.6 Receiving System; 4 Security and Privacy; 4.1 Data Encryption; 4.2 Access Policies and Controls; 4.3 Data Integrity; 4.4 Certifications.
5 Data Processing and Analysis5.1 Apache Spark; 5.2 Processing Pipeline; 5.3 Filtering; 6 Managed Computing Environments and Cloud Computing; 6.1 Databricks; 7 Data Storage and Formatting; 7.1 Raw JSON in AWS S3 Cloud Storage; 7.2 Parquet Binary Files and Streaming to Improve Efficiency; 8 Learning Science and Analytics; 8.1 Learning Curves for Learning Objectives; 8.2 Confidence and Metacognition; 9 Data Visualization; 9.1 Data Exploration and Visualization Using Built-in Tools; 10 Relationship Between Research and Production; 10.1 Development, Test, and Production Environment.
10.2 Software Development Life Cycle for Educational Apps11 Summary; References; Framing Learning Analytics and Educational Data Mining for Teaching: Critical Inferencing, Domain Knowledge, and Pedagogy; 1 Wired and Virtual Schools; 2 Learning Analytics and Educational Data Mining; 3 Implications for Teacher Training Validity and Inferencing; 4 Implications for Teacher Research-More Theory, Thicker Description; 5 Conclusion; References; Learning Traces, Competence Assessment, and Causal Inference for English Composition; 1 For Big Data in Education; 2 Competence; 3 Learning Traces.
4 The Next Step: Causal Models5 The Case Study; 6 Big Data Architecture; 7 Conclusion; References; QUESGEN: A Framework for Automatic Question Generation Using Semantic Web and Lexical Databases; 1 Introduction; 2 Technology-Enhanced Question Generation Systems; 3 A Framework for Generating Adaptive Questions; 3.1 The Conceptual Design; 3.2 The Template-Based Question Generation Approach and Implementation; 4 Term Relevance Analysis; 4.1 Methodology; 4.2 Results; 4.3 Discussion; 5 Question Ranking Evaluation; 5.1 Methodology.
5.2 Ranking Algorithm and Integration in the Question Generation Framework5.3 Evaluation; 5.4 Results and Discussion; 6 Conclusions; References; A Big Data Reference Architecture for Teaching Social Media Mining; 1 Introduction; 2 Foundation; 3 Solution Architecture; 4 Results; 4.1 Analysis of Twitter Sentiment Data of a U.S. Presidential Candidate; 4.2 Differences in the Usage of Twitter Between IOS and Android Device Users; 4.3 Analysis of Meetup RSVPs: How About Fake RSVPs; 5 Conclusion; References; Big Data in Education: Supporting Learners in Their Role as Reflective Practitioners.
5 Data Processing and Analysis5.1 Apache Spark; 5.2 Processing Pipeline; 5.3 Filtering; 6 Managed Computing Environments and Cloud Computing; 6.1 Databricks; 7 Data Storage and Formatting; 7.1 Raw JSON in AWS S3 Cloud Storage; 7.2 Parquet Binary Files and Streaming to Improve Efficiency; 8 Learning Science and Analytics; 8.1 Learning Curves for Learning Objectives; 8.2 Confidence and Metacognition; 9 Data Visualization; 9.1 Data Exploration and Visualization Using Built-in Tools; 10 Relationship Between Research and Production; 10.1 Development, Test, and Production Environment.
10.2 Software Development Life Cycle for Educational Apps11 Summary; References; Framing Learning Analytics and Educational Data Mining for Teaching: Critical Inferencing, Domain Knowledge, and Pedagogy; 1 Wired and Virtual Schools; 2 Learning Analytics and Educational Data Mining; 3 Implications for Teacher Training Validity and Inferencing; 4 Implications for Teacher Research-More Theory, Thicker Description; 5 Conclusion; References; Learning Traces, Competence Assessment, and Causal Inference for English Composition; 1 For Big Data in Education; 2 Competence; 3 Learning Traces.
4 The Next Step: Causal Models5 The Case Study; 6 Big Data Architecture; 7 Conclusion; References; QUESGEN: A Framework for Automatic Question Generation Using Semantic Web and Lexical Databases; 1 Introduction; 2 Technology-Enhanced Question Generation Systems; 3 A Framework for Generating Adaptive Questions; 3.1 The Conceptual Design; 3.2 The Template-Based Question Generation Approach and Implementation; 4 Term Relevance Analysis; 4.1 Methodology; 4.2 Results; 4.3 Discussion; 5 Question Ranking Evaluation; 5.1 Methodology.
5.2 Ranking Algorithm and Integration in the Question Generation Framework5.3 Evaluation; 5.4 Results and Discussion; 6 Conclusions; References; A Big Data Reference Architecture for Teaching Social Media Mining; 1 Introduction; 2 Foundation; 3 Solution Architecture; 4 Results; 4.1 Analysis of Twitter Sentiment Data of a U.S. Presidential Candidate; 4.2 Differences in the Usage of Twitter Between IOS and Android Device Users; 4.3 Analysis of Meetup RSVPs: How About Fake RSVPs; 5 Conclusion; References; Big Data in Education: Supporting Learners in Their Role as Reflective Practitioners.