Xia, MengXu, MinLin, Chuan-enCheng, Ta YingQu, HuaminMa, XiaojuanViola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana2020-05-242020-05-2420201467-8659https://doi.org/10.1111/cgf.13998https://diglib.eg.org:443/handle/10.1111/cgf13998Problem-solving dynamics refers to the process of solving a series of problems over time, from which a student's cognitive skills and non-cognitive traits and behaviors can be inferred. For example, we can derive a student's learning curve (an indicator of cognitive skill) from the changes in the difficulty level of problems solved, or derive a student's self-regulation patterns (an example of non-cognitive traits and behaviors) based on the problem-solving frequency over time. Few studies provide an integrated overview of both aspects by unfolding the problem-solving process. In this paper, we present a visual analytics system named SeqDynamics that evaluates students' problem-solving dynamics from both cognitive and non-cognitive perspectives. The system visualizes the chronological sequence of learners' problem-solving behavior through a set of novel visual designs and coordinated contextual views, enabling users to compare and evaluate problem-solving dynamics on multiple scales. We present three scenarios to demonstrate the usefulness of SeqDynamics on a real-world dataset which consists of thousands of problem-solving traces. We also conduct five expert interviews to show that SeqDynamics enhances domain experts' understanding of learning behavior sequences and assists them in completing evaluation tasks efficiently.Attribution 4.0 International LicenseHuman centered computingVisual analyticsApplied computingElearning10.1111/cgf.13998511-522