| Document Type
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BL
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| Record Number
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889676
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| Title & Author
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Frontiers of cyberlearning : : emerging technologies for teaching and learning /\ J. Michael Spector [and eight others], editors.
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| Publication Statement
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Singapore :: Springer,, [2018]
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| Series Statement
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Lecture notes in educational technology
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| Page. NO
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1 online resource
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| ISBN
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9789811306501
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: 9811306508
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9789811306495
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9811306494
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| Bibliographies/Indexes
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Includes bibliographical references.
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| Contents
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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.
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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.
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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.
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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.
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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.
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| Abstract
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This book demonstrates teachers' and learners' experiences with big data in education; education and cloud computing; and new technologies for teacher support. It also discusses the advantages of using these frontier technologies in teaching and learning and predicts the future challenges. As such, it enables readers to better understand how technologies can improve learning and teaching experiences. It is intended for graduates and scholars in educational technology disciplines and anyone interested in the applications of frontier technologies in education.
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| Subject
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Computer-assisted instruction.
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Educational innovations.
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Educational technology.
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artificial intelligence
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big data
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cloud computing
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Computer-assisted instruction.
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distance learning
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education
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Educational innovations.
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Educational technology.
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higher education
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information science
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innovation
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Internet
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new educational methods
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new technology
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programmed learning
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semantic web
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social media
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| Dewey Classification
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371.33/4
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| LC Classification
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LB1027.F76 2018
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| NLM classification
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32.16.28.20ep-class
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32.16.32ep-class
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| Added Entry
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Spector, J. Michael
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