Frontiers of Cyberlearning

1. Front Matter

   Pages i-vi

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2. Learning Any Time, Anywhere: Big Educational Data from Smart Devices

   • Mark A. Riedesel, Patrick Charles

   Pages 1-31

3. Framing Learning Analytics and Educational Data Mining for Teaching: Critical Inferencing, Domain Knowledge, and Pedagogy

   • Owen G. McGrath

   Pages 33-47

4. Learning Traces, Competence Assessment, and Causal Inference for English Composition

   • Clayton Clemens, Vivekanandan Kumar, David Boulanger, Jérémie Seanosky, Kinshuk

   Pages 49-67

5. QUESGEN: A Framework for Automatic Question Generation Using Semantic Web and Lexical Databases

   • Nguyen-Thinh Le, Alexej Shabas, Patrick McLaren

   Pages 69-89

6. A Big Data Reference Architecture for Teaching Social Media Mining

   • Jochen Wulf

   Pages 91-101

7. Big Data in Education: Supporting Learners in Their Role as Reflective Practitioners

   • Sabine Seufert, Christoph Meier

   Pages 103-123

8. Towards Big Data in Education: The Case at the Open University of the Netherlands

   • Hubert Vogten, Rob Koper

   Pages 125-143

9. Learning Analytics in Practice: Providing E-Learning Researchers and Practitioners with Activity Data

   • J. Minguillón, J. Conesa, M. E. Rodríguez, F. Santanach

   Pages 145-167

10. Using Apache Spark for Modeling Student Behavior at Scale

    • Nicholas Lewkow, Jacqueline Feild

    Pages 169-176

11. Towards a Cloud-Based Big Data Infrastructure for Higher Education Institutions

    • Stefaan Ternier, Maren Scheffel, Hendrik Drachsler

    Pages 177-194

12. Cloud Services in Collaborative Learning: Applications and Implications

    • Ding-Chau Wang, Yong-Ming Huang

    Pages 195-210

13. Cloud Computing Environment in Big Data for Education

    • Dharmpal Singh

    Pages 211-233

14. Head in the Clouds: Some of the Possible Issues with Cloud Computing in Education

    • Richard A. W. Tortorella, Kinshuk, Nian-Shing Chen

    Pages 235-246

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.

• Big Data in Education
• Cloud Computing in Education
• Learning Analytics
• Technology-rich Classroom
• Cyberlearning
• Future Learning Environments
• Cloud Computing Environment
• Online Learning

• Department of Learning Technologies, University of North Texas, Denton, USA

  J. Michael Spector

• School of Computing and Information System, Athabasca University, Athabasca, Canada

  Vivekanandan Kumar

• Analytics and R&D, McGraw-Hill Education, Boston, USA

  Alfred Essa

• Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan

  Yueh-Min Huang

• Open University in the Netherlands, Heerlen, The Netherlands

  Rob Koper

• School of Computing, University of Eastern Finland, Joensuu, Finland

  Richard A. W. Tortorella

• Smart Learning Institute, Beijing Normal University, Beijing, China

  Ting-Wen Chang

• School of Educational Technology, Beijing Normal University, Beijing, China

  Yanyan Li, Zhizhen Zhang

Johnathan Michael Spector is a professor at the Study Technology Department, School of Information, University of North Texas and former chairman of the Association of American Education and Communication Technology.




Vivekanandan Kumar is a professor at the School of Computing and Information Systems at Athabasca University, Canada. He holds the Natural Sciences and Engineering Research Council of Canada’s Discovery Grant on Anthropomorphic Pedagogical Agents, funded by the Government of Canada.




Alfred Essa is vice president of Analytics and R&D at McGraw-Hill Education, where he leads the company-wide learning science research and data science practices. Previously he was director of Analytics Research & Strategy at Desire2Learn Inc, where he was responsible for research and business strategy for the analytics portfolio.




Yueh-Min Huang is a chair professor of the Multimedia Network Lab at the Department of Engineering Science, National Cheng Kung University. His research interests include multimedia communications, e-learning, wireless networking and artificial intelligence.




Rob Koper is a distinguished professor at the Open University of the Netherlands with a specific focus on the innovation of online education. He studies the social and cognitive aspects of human learning, the effective use of ICTs for human learning and the improvement of educational institutions to facilitate teaching and learning.




Richard A. W. Tortorella received his MSc degree from Athabasca University, Canada in 2013 and is currently a PhD candidate at the University of Eastern Finland. His research interests include context-aware learning systems, m-Learning and artificial intelligence.




Ting-Wen Chang is an associate research fellow and the director of the International Cooperation Center at Beijing Normal University’s Smart Learning Institute. His research focuses on smart learning as is involved in numerous international cooperation projects.




Yanyan Li is a professor at Beijing Normal University’s Smart Learning Institute. Her research interests include computer-supported collaborative learning, learning analytics, and STEAM education.




Zhizhen Zhang is a lecturer at the School of Educational Technology, Faculty of Education at Beijing NormalUniversity. His research interests include teacher professional learning, technology-enhanced science education and education software development.

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