Chapter 6 provides a summary of the topics around the Community of Inquiry, big data frameworks and tools, and additional commentary on these constructs. Additionally, the authors provide a concrete example of research work that has been updated with use of emerging big data technologies, provide concrete advice for future researchers working in these same or similar research areas, and describe further insights and sharing of the authors' research as it connects to constructs related to the CoI framework and online teaching and learning. Finally, the chapter includes predictions for future trends relating to big data and the constructs of the Community of Inquiry. Overall predictions are towards automated data analysis tools that are capable of looking into newer areas of analyses such as affective computing. A list of additional readings is included.
TopThis brief Research Insights publication provides an updated overview on the Community of Inquiry (CoI) Framework, drawing on the original framework (Garrison, Anderson, & Archer, 2000; Garrison & Arbaugh, 2007) as well as more current and recent literature. This work reaffirms it as a solid and robust foundation for a wide variety of audiences, including educational researchers, practitioners, instructional designers, and administrators who deal with online and blended learning in its various forms. Our review of the concepts and usefulness of CoI as both an educational design framework and a research framework potentially supports the work of analytics-focused educators, practitioners, and administrators in K-12 and higher education, as well as those active within the broader online teaching and learning spaces, including those who work in social media spaces across various contexts.
Although the literature on Community of Inquiry is scarce in research in K-12 educational spaces, the use of personalized learning, mobile learning, learning management systems, and data analytics has made some headway into K-12 spaces (e.g., Johnson, Adams Becker, Estrada, & Freeman, 2015), setting the stage for the use of big data overlaid onto a CoI framework. However, there is a scarce amount of empirical research of analytics-focused data centering around the COI framework in K-12, including the literature on virtual schooling, online learning, and K-12. Literature in K-12 that draws on CoI is, however, increasing (e.g., Borup, Graham, & Drysdale, 2014; Xiong, Ge, Wang, & Wang, 2017). We see a need for much more research in this area.
Specifically as authors writing over the course of these chapters, we have argued that the CoI framework is a useful research framework when combined with an overall big data approach to the examination of online learning in its various forms (e.g., blended, hybrid, mobile learning, and more informal social media-based learning). Additionally, we suggest that over the years that CoI has come into play for researchers and instructional designers, the notion of learning has expanded beyond the typical learning management system within a traditional online course to also include such spaces as social media and a much broader set of data.
Within the big data approach, we specifically focus on several types of analytics including Machine Learning (ML), Natural Language Processing (NLP), Social Network Analysis (SNA), and other emerging research approaches. As analytics-focused researchers in this area, our environments for data collection are changing daily, and now include MOOCS (Massively Open Online Courses), online professional development in its varying forms including mobile learning, and, as shared throughout this text, social media based learning, especially Twitter spaces. These research contexts were also reviewed to include professional development and training, informal training, and increasingly to feature online spaces in the K-12 educational arena.
The early chapters of this book provided a foundation--an introduction to knowledge and understanding of the CoI framework. Later chapters described how this research area increasingly intersects with approaches and analyses using big data and learning analytics. Chapter three in particular provided an overview on current CoI research which featured “big data” in volume or variety, as well as research marked by emerging toolsets for a big data or machine learning approach to analysis. Later chapters described this emerging toolset landscape in more detail, as well as the applications of the CoI framework and toolsets together in the larger and more complex settings of an institution or on a network scale. In this chapter we provide our predictions for future trends, both by drawing on our own experience and knowledge base, as well as the extant literature in the field. Finally, we provide advice to researchers interested in the field the literature as it pertains to the CoI and big data research.