Strategies and difficulties during students’ construction of data visualizations

Background: Data visualizations transform data into visual representations such as graphs, diagrams, charts and so forth, and enable inquiries and decision-making in many professional fields, as well as in public and economic areas. How students’ data visualization literacy (DVL), including constructing, comprehending, and utilizing adequate data visualizations, can be developed is gaining increasing attention in STEM education. As fundamental steps, the purpose of this study was to understand common student difficulties and useful strategies during the process of constructing data visualization so that suggestions and principles can be made for the design of curricula and interventions to develop students’ DVL. Methods: This study engaged 57 college and high school students in constructing data visualizations relating to the topic of air quality for a decision-making task. The students’ difficulties and strategies demonstrated during the process of data visualization were analyzed using multiple collected data sources including the students’ think-aloud transcripts, retrospective interview transcripts, and process videos that captured their actions with the data visualization tool. Qualitative coding was conducted to identify the students’ difficulties and strategies. Epistemic network analysis (ENA) was employed to generate network models revealing how the difficulties and strategies co-occurred, and how the college and high school students differed. Results: Six types of student difficulties and seven types of strategies were identified. The strategies were further categorized into non-, basic- and high-level metavisual strategies. About three-quarters of the participants employed basic or high-level metavisual strategies to overcome the technological and content difficulties. The high school students demonstrated a greater need to develop content knowledge and representation skills, whereas the college students needed more support to know how to simplify data to construct the best data visualizations. Conclusions and implications: The study specified metacognition needed for data visualization, which builds on and extends the cognitive model of drawing construction (CMDC) and theoretical perspectives of metavisualization. The results have implications for developing students’ data visualization literacy in STEM education by considering the difficulties and trajectories of metacognitive strategy development, and by addressing the different patterns and needs demonstrated by the college and high school students.