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"


Miss Meng XIA


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:

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 ****