39-Issue 3

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https://diglib.eg.org/handle/10.2312/2632882

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Browsing 39-Issue 3 by Subject "E"

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    SeqDynamics: Visual Analytics for Evaluating Online Problem-solving Dynamics
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Xia, Meng; Xu, Min; Lin, Chuan-en; Cheng, Ta Ying; Qu, Huamin; Ma, Xiaojuan; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Problem-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.