SeqDynamics: Visual Analytics for Evaluating Online Problem-solving Dynamics
| dc.contributor.author | Xia, Meng | en_US |
| dc.contributor.author | Xu, Min | en_US |
| dc.contributor.author | Lin, Chuan-en | en_US |
| dc.contributor.author | Cheng, Ta Ying | en_US |
| dc.contributor.author | Qu, Huamin | en_US |
| dc.contributor.author | Ma, Xiaojuan | en_US |
| dc.contributor.editor | Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana | en_US |
| dc.date.accessioned | 2020-05-24T13:01:45Z | |
| dc.date.available | 2020-05-24T13:01:45Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | 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. | en_US |
| dc.description.number | 3 | |
| dc.description.sectionheaders | Visual Analytics for Problem Solving | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 39 | |
| dc.identifier.doi | 10.1111/cgf.13998 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 511-522 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13998 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13998 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | ] |
| dc.subject | Human centered computing | |
| dc.subject | Visual analytics | |
| dc.subject | Applied computing | |
| dc.subject | E | |
| dc.subject | learning | |
| dc.title | SeqDynamics: Visual Analytics for Evaluating Online Problem-solving Dynamics | en_US |