Visualizing Qualitative Data: Unpacking the Complexities and Nuances of Technology-Supported Learning Processes

Analyzing qualitative data from learning processes is considered "messy" and time consuming (Chi in J Learn Sci 6(3):271-315, 1997). It is often challenging to summarize and synthesize such data in a manner that conveys the richness and complexity of learning processes in a clear and concise manner. Moreover, qualitative data often contains patterns that are not immediately apparent. Consequently, visualization can be an effective tool for representing and unpacking the complexities and multidimensions of learning processes. Additionally, visualizations provide a time-efficient approach to analyzing data and a high-level view of the learning process over time for researchers to zoom in on intriguing moments and patterns (Huang et al. in Comput Human Behav 87:480-492, 2018). In this conceptual paper, we provide a broad overview of research in the field of visualizing qualitative data and discuss two studies (1) visualizing role-changing patterns in an interdisciplinary learning environment and (2) operationalizing collaborative computational thinking practices via visualization. By leveraging these studies, we aim to demonstrate a visualization processing flow along with qualitative research and methods. Particularly, the processing flow includes three critical elements: research subjectivity, complexity of visual encoding, and purpose of visual encoding. The discussion highlights the iterative and creative nature of the visualization technique. Furthermore, we discuss the benefits, challenges, and limitations of using visualization in the context of qualitative studies.