QLens: Visual Analytics of Multi-step Problem-solving Behaviors for Improving Question Design

Meng Xia, Reshika Palaniyappan Velumani, Yong Wang, Huamin Qu, Xiaojuan Ma

View presentation: 2020-10-28T16:15:00Z GMT-0600 Change your timezone on the schedule page
Exemplar figure
QLens enables question designers to analyze students' multi-step problem-solving behaviors for design improvements from levels of detail. (1) Macro-level: Overview (a) shows the question's preview (a1), students' overall performance (a2) and the ranking of common wrong answers based on the frequency of occurence(a3). (2) Meso-level: Transition View (b) visualizes the problem-solving processes to reflect how a group of students proceed step by step (problem-solving logic) using a novel glyph-embedded Sankey diagram (b2) and the amount of efforts (engagement) using a contextual axis (b3). In addition, Comparison View (c) enables users to compare the problem-solving logic, engagement, and encountered difficulties of different groups (c1, c2, c3). (3) Micro-level (the highlighted path in b): typical incorrect paths and the corresponding data-driven recommended paths are demonstrated for question designers to evaluate the feasibility of the data-driven feedback. Rich interactions are also provided for exploration (e.g., filters in b1).

Learning Behavior Analysis, Visual Analytics, Time Series Data


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.