The primary challenge of online education is bridging the distance, both geographical and psychological, between student-and-teacher and student-and-student dynamics. In today’s increasingly digitalized world, it is important to enhance the quality of learning and the nature of interactions in distance education formats.

The Community of Inquiry Framework in Contemporary Education: Emerging Research and Opportunities is a critical scholarly resource that examines the benefits, challenges, and intricacies of online learning with attention to key concepts, literature, resources, tools, and scenarios. Featuring coverage on a broad range of topics, such as big data research, network communication theory, educational data mining, and digital learning, this book is geared towards researchers, instructors, and higher education administrators seeking current research on the integration of new distance learning technologies.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Analytics
Artificial Intelligence
Big Data Research
Communication
Data Analytics
Digital Learning
Educational Data Mining
Machine Learning

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Editor/Author Biographies

Peggy Semingson is an Associate Professor of Curriculum and Instruction at The University of Texas at Arlington where she teaches online courses in Literacy Studies. Dr. Semingson has experience as a classroom teacher and reading specialist in both Southern California and Texas. Her research interests include social contexts of literacy learning, digital pedagogies, and online literacy teacher education. She currently also studies the ways that we can use digital pedagogies to engage pre-service and in-service teachers to most effectively help them to teach literacy in their current and future classroom contexts. Within this area, she is interested in knowledge sharing that takes place online, distributed cognition, and video-mediated (e.g., YouTube) discussion and dialogue. She has published in Teachers College Record, Language Arts, and Research in the Teaching of English. She was awarded the Jeanne S. Chall Research Grant from Harvard University in 2009-2010 and was awarded the prestigious 2013 USDLA Best Practices Platinum Award for Excellence in Distance Learning Teaching. In 2010 she was awarded the President’s Award for Excellence in Distance Education Teaching at UT Arlington. Most recently she won the 2017 International Literacy Association (ILA) Jerry Johns Outstanding Teacher Educator in Reading Award.

Pete Smith is Chief Analytics Officer and Senior Associate Vice President at the University of Texas Arlington, where he founded and oversees the office of University Analytics, and co-founded the Learning Innovation and Networked Knowledge (LINK) lab, a learning analytics research laboratory. He is a Professor of University Studies, and he previously served for 18 years as Vice Provost for Digital Teaching and Learning. He earned his B.A. and B.S. degrees from the Pennsylvania State University, and his Master’s in Slavic Languages and Doctorate in Second Language Acquisition at the University of Texas at Austin. His teaching and research focuses on natural language processing and understanding, translation automation, and “big data” in education. He also oversees UTA’s Localization and Translation certificate program, offered to students of seven languages (Arabic, Chinese, French, German, Korean Portuguese, Russian) as an introduction to localization and the localization industry. And he was recently recognized by the United States Distance Learning Association with a national award for “Outstanding Leadership by an Individual in the Field of Distance Learning,” for his role leading UTA to become a recognized leader in online learning.

Henry Anderson is a Data Scientist in the University of Texas at Arlington’s University Analytics group and a part-time researcher for the Learning Innovation and Network Knowledge (LINK) research laboratory. He holds undergraduate physics and linguistics degrees from Rice University. As a data scientist, he works to incorporate new data sources and practices into the university’s workflow, build large predictive and exploratory models around student behavior and university outcomes, and evangelize the mindset and tools found in big data and machine learning. As a researcher, his primary interests are in how language is used online to enforce and create community norms, construct and represent identity, and reveal behavioral patterns and information about the speaker, and how language can be used in concert with other quantitative data sources to provide deeper and more meaningful insights than either source on their own. Through this lens, he has approached questions about the regulation of discourse in professional societies, the development of students’ world views and integrated thought processes as evidenced through their writing, and the nature and significance of systematic, dialect-like differences in language use across online communities.

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