With the rapid development of online education in recent years, there has
been an increasing number of learning platforms that provide students with
multi-step questions to cultivate their problem-solving skills. To guarantee
the high quality of such learning materials, question designers need to inspect
how students' problem-solving processes unfold step by step to infer whether
students' problem-solving logic matches their design intent. They also need to
compare the behaviors of different groups (e.g., students from different
grades) to distribute questions to students with the right level of knowledge.
The availability of fine-grained interaction data, such as mouse movement
trajectories from the online platforms, provides the opportunity to analyze
problem-solving behaviors. However, it is still challenging to interpret,
summarize, and compare the high dimensional problem-solving sequence data. In
this paper, we present a visual analytics system, QLens, to help question
designers inspect detailed problem-solving trajectories, compare different
student groups, distill insights for design improvements. In particular, QLens
models problem-solving behavior as a hybrid state transition graph and
visualizes it through a novel glyph-embedded Sankey diagram, which reflects
students' problem-solving logic, engagement, and encountered difficulties. We
conduct three case studies and three expert interviews to demonstrate the
usefulness of QLens on real-world datasets that consist of thousands of
problem-solving traces
