THESIS
2020
xvi, 148 pages : color illustrations ; 30 cm
Abstract
As problem-solving skills are becoming increasingly important for citizens, many question-based
learning platforms emerge to provide students with online questions to cultivate
their problem-solving skills. For example, various MOOC platforms (e.g., Khan Academy),
online question pools (e.g., LeetCode) offer interactive maths questions and programming
exercises. The distance gap, time gap, and the imbalanced number of educators and students
make it hard for educators and students to communicate in the problem-solving
processes. For educators, they need to understand students’ problem-solving logic and
learning habits to improve question designs and give customized instructions to groups
with different cognitive abilities and non-cognitive traits. For students, they can improve...[
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As problem-solving skills are becoming increasingly important for citizens, many question-based
learning platforms emerge to provide students with online questions to cultivate
their problem-solving skills. For example, various MOOC platforms (e.g., Khan Academy),
online question pools (e.g., LeetCode) offer interactive maths questions and programming
exercises. The distance gap, time gap, and the imbalanced number of educators and students
make it hard for educators and students to communicate in the problem-solving
processes. For educators, they need to understand students’ problem-solving logic and
learning habits to improve question designs and give customized instructions to groups
with different cognitive abilities and non-cognitive traits. For students, they can improve
their self-learning skills based peers’ learning data. For example, regulating their learning
habits and planning personalized learning paths. However, it is challenging for educators
and students to analyze and learn from thousands of problem-solving sequences. Visualization technologies turn out to be an effective solution to support data presentation and
analytics in the aforementioned scenarios.
In this thesis, we enhance the communication between educators and students in the
context of the online problem solving by a visualization approach. On the one hand,
we present two visual analytics systems for educators to understand students’ problem-solving
behaviors from two levels respectively. The first system, QLens, helps question designers
analyze students’ problem-solving behaviors in multi-step questions to improve
question designs at a micro-level. The second system, SeqDynamics, evaluates students’
problem-solving dynamics from both cognitive and non-cognitive perspectives at a macro
level. On the other hand, we try to improve their self-learning skills, which includes learning
regulation and learning planning. We use information visualization to promote students’
reflection on “Gaming the system” behavior and reduce the gaming behaviors. In
addition, we propose PeerLens, an interactive visual analytics system that enables peer-inspired
learning path planning. We have conducted various quantitative evaluations,
case studies, user studies, and expert interviews to demonstrate the effectiveness and usefulness
of our proposed visualization approaches.
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