Introducing Visual Representations for Studying Collaborative Inquiry

• In Event: Research Methodologies for Studying Problem-Based and Project-Based Learning

Abstract

Goal
Problem-based learning (PBL) researchers face challenges in understanding and analyzing data when translating PBL into technology-mediated complex learning environments. Visual representations or visualizations can be used for simplifying difficult-to-comprehend information, illuminating patterns and complex relationships, and enhancing overall readability and accessibility of data (Munzner, 2014; Ware, 2019). The goal of this paper is to present several methods of visualizing student activity and collaboration in PBL in ways that can augment understanding of the complexity within PBL classrooms and to provide insights into the use of specific visual representations to address research questions in PBL.

Perspective
The current study is grounded in sociocultural theory, where learners socially co-construct meanings by interacting with others, learning content, and mediating tools in a systemic environment (Danish et al., 2018, Palincsar, 1998). With the perspective, we consider visual representations an aid to mediate researchers’ interpretation of the multiple data streams generated in PBL, including video, computer log data, gestures, and student artifacts as well as other forms of data such as tests and surveys.

Visual Representations
We presented the visual representations that have been widely used in PBL. For quantitative analysis, we introduce Social Network System (SNA; Scott, 2013), Structural Equation Model (SEM; Hox & Bechger, 1998), and path analysis. In terms of qualitative analysis, we exhibit Chronologically-Ordered Representation of Discourse and Tool-Related Activity (CORDTRA), Event map, and Spatial representations of physical activity. In each method, we will illustrate purposes of its use, research questions frequently used a certain visualization, and its features. By reviewing articles that utilized these representations in PBL, we present several examples of how these visualizations were helpful in interpreting complex data and illuminating how students learn in PBL and other forms of collaborative inquiry. We found that SNA (Zheng et al., 2021; Tao & Zhang, 2021), SEM (Stage & Nora, 2004) and path analysis (Noordzij & Wijnia, 2000; Schmidt & Moust, 2000), which usually deal with relatively large amounts of data, have been widely used to identify interaction patterns among students, teachers, and tools and see how the patterns change over time. CORDTRA (Hmelo-Silver et al., 2008), Event map (Green & Bridges, 2018), and the spatial representations (Bridges et al., 2020) have been utilized to represent how learners develop understanding through multidimensional interactions with each other and with the tools in PBL classrooms.

Discussion
Although such visual representation methods enable us to visualize and trace complex dynamics and communicate findings with readers, they are not self-explanatory. We must be cautious about potential pitfalls that could lead to misinterpretation by employing a good understanding of how to interpret the elements of the visualizations as well as the visualizations themselves. Therefore, proper training for PBL researchers in using visualizations is crucial to fully harness the benefits of these tools and make informed inferences about the intricate learning environments within PBL. With the appropriate use of these visualizations, researchers can catch and make sense of meaningful and interesting interaction moments and patterns that might otherwise be hidden in the data record.

Authors

• Daeun Hong, Indiana University

• Srijita Chakraburty, Indiana University

• Xiaotian Zou, Indiana University

• Yuxin Chen, University of South Alabama

• Cindy E. Hmelo-Silver, Indiana University