Bridge the Gap between Educators and Students in Online Learning: A Visualization Approach based on Problem-solving Data

PhD Thesis Proposal 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. 
Different from traditional learning, educators (e.g., instructors) and 
students have limited communications and interactions online due to their 
unbalanced numbers. It is necessary to bridge the gap between educators 
and students for effective instruction and learning 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 get a sense of what are the best learning habits 
and learning paths to acquire the knowledge that suggested by educators. 
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 proposal, 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 designed a set of visualizations to guide students' learning by 
visualizing educators' instructions and peers' learning data in the 
real-world learning environment. The visualization aims to persuade 
students to improve their reflection on "gaming the system" behavior and 
regulate their learning. 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:                   Monday, 18 May 2020

Time:                   3:30pm - 5:30pm

Zoom Meeting:           https://hkust.zoom.us/j/99865439913

Committee Members:      Dr. Xiaojuan Ma (Supervisor)
                        Prof. Huamin Qu (Supervisor)
                        Dr. Raymond Wong (Chairperson)
                        Prof. Ting-Chuen Pong


**** ALL are Welcome ****