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Bridge the Gap between Educators and Students in Online Learning: A Visualization Approach based on Problem-solving Data
The Hong Kong University of Science and Technology Department of Computer Science and Engineering PhD Thesis Defence Title: "Bridge the Gap between Educators and Students in Online Learning: A Visualization Approach based on Problem-solving Data" By Miss Meng XIA Abstract With online education becoming popular in the past decades, there has been an increasing number of learning platforms that 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/or programming exercises. However, the distance gap, time gap, and imbalance in the number of educators and students make it challenging for educators to give customized instructions and for students to achieve personalized learning. It is necessary to bridge the gap between educators and students for effective instruction and learning, especially in the problem-solving process. For educators, they need to understand students' problem-solving logic when solving a multi-step question and also students’ learning habits when solving a series of questions. Based on the understanding, they can improve question designs and give customized instructions to groups with different cognitive abilities and non-cognitive traits. For students, they need to improve their self-learning skills. For example, planning the personalized learning paths and regulating their learning habits. However, it is challenging to present high-dimensional problem-solving sequences intuitively and support different analytical tasks (e.g., comparison) for educators as well as students. 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 planning and learning regulation. We propose PeerLens, an interactive visual analytics system that enables peer-inspired learning path planning. In addition, we use information visualization to promote students' reflection on "Gaming the system" behavior and reduce the gaming behaviors. We have conducted various quantitative evaluations, case studies, user studies, and expert interviews to demonstrate the effectiveness and usefulness of our proposed systems and the visualization designs for problem-solving data analysis. Date: Tuesday, 11 August 2020 Time: 10:30am - 12:30pm Zoom Meeting: https://hkust.zoom.us/meeting/96419641299 Chairman: Prof. Charles CHAN (HUMA) Committee Members: Prof. Xiaojuan MA (Supervisor) Prof. Huamin QU (Supervisor) Prof. Andrew HORNER Prof. Ke YI Prof. Weichuan YU (ECE) Prof. Nancy LAW (HKU) **** ALL are Welcome ****