E-Learning Force

Empowering the future of learning via visualization, smart, and future technologies!


Our research vision is to create an exhilarating and transformative learning experience that will redefine education. By harnessing the immense potential of cutting-edge technologies like artificial intelligence, big data and visualization, and immersive technologies, we are on a mission to revolutionize how students learn and engage in the future.

Through personalized learning experiences, streamlined administrative tasks, and invaluable insights derived from student data, we empower educators to make informed instructional decisions and unleash unprecedented levels of student engagement. Moreover, by leveraging the immersive power of technologies like AR/VR, we offer students an unparalleled opportunity to delve into intricate concepts, simulate real-world scenarios, and embark on captivating virtual experiments that ignite their enthusiasm and amplify their energy. Together, these groundbreaking advancements will shape a dynamic and profoundly impactful educational landscape, perfectly tailored for the IT generation of passionate learners.

We are the dynamic e-learning force of HKUST VisLab, a renowned research group in educational technology and visulization. We keep a track record of securing substantial funding, receiving best paper awards, and gaining recognition from the industry. Whether you are interested in joining us, collaborating with us, or supporting our endeavors, we welcome your involvement. For further information, please don't hesitate to reach out to us through the contact details provided.

Artificial Intelligence

Enables personalized learning experiences, automates administrative tasks, enhances student engagement, and supports continuous improvement.

Big Data & Visualization

Empowers educators to extract valuable insights from student data, Helps maing informed instructional decisions, and improves student engagement.

Immersive Technologies

Supports students exploring complex concepts, simulates real-world scenarios, and participates in virtual experiments with more energy and enthusiasm.


2023-Current (QEF Fund)

Al-assisted Virtual Reality English Speaking Program for Secondary Students

Our project proposal seeks to revolutionize English language education by addressing common challenges faced by learners. By bringing together the expertise of academic institutions, technology companies, and schools, our project aims to empower English language learners, offering them an immersive language tutoring system within the metaverse. By leveraging AI technology, an AI tutor will provide learners with prompt and expert feedback, allowing them to enhance their proficiency in real-time. Implemented within a virtual reality environment, the system will enable learners to practice and refine their English communication skills through simulated real-world scenarios. This innovative approach aims to create an engaging and authentic learning experience, enhancing language proficiency and building learners' confidence in using English in practice.

2023-Current (ITF Fund)

AI-powered, Personalized Analysis & Feedback Platform for K-12 Comp. Thinking Skills

Teaching computational thinking methods is vital in preparing K-12 students ready for the information age. Online learning platforms supplement traditional schooling but lack efficient monitoring and guidance due to information gaps and time constraints. Therefore, we proposed to revolutionize K-12 education and empower students to excel in the technology-driven landscape of the information age by reducing the workload of teachers and parents, facilitating efficient analysis of student performance, and providing personalized guidance. To achieve this goal, we will design a visual analytics system; innovate an automated reporting system; and invent a personalized learning path recommendation system. Our work will be deployed and rigorously tested in real-world settings, ensuring tangible and impactful results.

2021-Current (ITF Fund)

AI-powered Audience Engagement Analysis for Virtual Communication

Virtual communication continues to demonstrate its significance in a post-COVID world. However, measuring and analyzing audience engagement in this context remains challenging. Our project aims to develop an analytics method for assessing audience engagement in virtual communication. Through three modules - feature extraction, data labeling, and engagement analytics - we collect multi-modal data. By merging these features and utilizing machine learning models, we extract valuable insights into audience engagement. This information benefits both speakers and listeners by facilitating the evaluation of presentation effectiveness and understanding of communication behaviors. The techniques will be applied and evaluated on popular call-tech platforms such as Zoom, Microsoft Teams, and Google Meet, potentially impacting a range of industries.

2016-2022 (ITF Fund)

An Open Learning Design, Data Analytics and Visualization Framework for E-Learning

This project developed visualization techniques to analyze large-scale data from MOOCs, assisting course instructors and education analysts. The techniques allow exploration of various aspects, including video popularity, clickstream analysis, demographic distribution, user forum interactions, dropout analysis, and sequence analysis. An innovative interaction data collection technique was created to collect fine-grained mouse movement data during problem-solving on a K-12 E-Learning platform. The data was used to analyze problem-solving sequences and develop intuitive visualization systems like QLens and SeqDynamics, helping infer students' cognitive states. The main goal of this project is to provide intuitive and interactive analysis, allowing stakeholders to understand learners' behaviors and improve course designs with evidence-based guidance.





Merit in E-Learning at APICTA'15

HKICT Best Innovation Silver Award

Best Poster in IEEE VIS'2019



Knowledge Compass: A Question Answering System Guiding Students with Follow-Up Question Recommendations

ACM UIST, 2023

Author: Rui Sheng, Leni Yang, Haotian Li, Yan Luo, Ziyang Xu, Zhilan Zhou, David Gotz, Huamin Qu

Links: [ACM Library]

CollectiAR: Computer Vision-Based Word Hunt for Children with Dyslexia

ACM CHI Play, 2023

Author: Danlu Fei, Ze Gao, Linping Yuan, Zikai Alex Wen

Links: [ACM Library], [Presentation]

Designing a Data Visualization Dashboard for Pre-Screening Hong Kong Students with Specific Learning Disabilities


Author: Ka Yan Fung, Zikai Alex Wen, Haotian Li, Xingbo Wang, Shenghui Song, Huamin Qu

Links: [PDF]

BlockLens: Visual Analytics of Student Coding Behaviors in Block-Based Programming Environments

ACM L@S, 2022

Author: Sean Tsung, Huan Wei, Haotian Li, Yong Wang, Meng Xia, Huamin Qu

Links: [PDF]

A Visual Analytics Approach to Facilitate the Proctoring of Online Exams

ACM CHI, 2021

Author: Haotian Li, Min Xu, Yong Wang, Huan Wei, Huamin Qu

Links: [PDF]

Data Analytics and Visualization Approaches for Online Learning of Math and Computational Thinking


Author: HKUST VisLab E-Learning Group

Links: [PDF]

Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network

CIKM, 2020

Author: Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin Qu

Links: [PDF]

Using Information Visualization to Promote Students’ Reflection on “Gaming the system” in Online Learning

L@S, 2020

Author: Meng Xia, Yuya Asano, Joseph Jay Williams, Huamin Qu, Xiaojuan Ma

Links: [PDF]

SeqDynamics: Visual Analytics for Evaluating Online Problem-solving Dynamics

EuroVis, 2020

Author: Meng Xia, Min Xu, Chuan-en Lin, Ta Ying Cheng, Huamin Qu, Xiaojuan Ma

Links: [PDF]

Predicting Student Performance in Interactive Online QuestionPools Using Mouse Interactions

ACM LAK (Learning Analytics & Knowledge), 2020

Author: Huan Wei, Haotian Li, Meng Xia, Yong Wang, Huamin Qu

Links: [PDF]

Visual Analytics of Student Learning Behaviors on K-12 Mathematics E-learning Platforms

IEEE VIS Posters, 2019 (Best Poster Award)

Author: Meng Xia, Huan Wei, Min Xu, Leo Yu Ho Lo, Yong Wang, Rong Zhang, Huamin Qu

Links: [PDF]

MOOCad: Visual Analysis of Anomalous Learning Activities in Massive Open Online Courses

EuroVis 2019 Short Paper

Author: Xing Mu, Ke Xu, Qing Chen, Fan Du, Yun Wang, Huamin Qu

Links: [PDF]

PeerLens: Peer-inspired Interactive Learning PathPlanning in Online Question Pool

ACM CHI, 2019

Author: Meng Xia, Mingfei Sun, Huan Wei, Qing Chen, Yong Wang, Lei Shi, Huamin Qu, Xiaojuan Ma

Links: [PDF]

StageMap: Extracting and Summarizing Progression Stages in Temporal Event Sequences

IEEE Conference on Big Data, 2018

Author: Yuanzhe Chen, Abishek Puri, Linping Yuan, Huamin Qu

Links: [PDF]

ViSeq: Visual Analytics of Learning Sequence in Massive Open Online Courses​​

IEEE Transactions on Visualization & Computer Graphics, 2018

Author: Qing Chen, Xuanwu Yue, Xavier Plantaz, Yuanzhe Chen, Conglei Shi, Ting-Chuen Pong, Huamin Qu

Links: [PDF]

VisForum: A visual analysis system for exploring user groups in online forums

ACM Transactions on Interactive Intelligent Systems (ACM TiiS), 2018

Author: Siwei Fu, Yong Wang, Yi Yang, Qingqing Bi, Fangzhou Guo, Huamin Qu

Links: [PDF]

Visual Analytics and Storytelling of Data from Massive Open Online Courses

HKUST PhD Thesis, 2018

Author: Qing Chen (Supervisor: Huamin Qu)

Visual Analytics of Online Communication

HKUST PhD Thesis, 2018

Author: Siwei Fu (Supervisor: Huamin Qu)

Visual Analytics of Temporal Event Data

HKUST PhD Thesis, 2018

Author: Yuanzhe Chen (Supervisor: Huamin Qu)

Visual Analysis of MOOC Forums with iForum

IEEE Transactions on Visualization & Computer Graphics, 2017

Author: Siwei Fu, Jian Zhao, Weiwei Cui, Huamin Qu

Links: [PDF]

DropoutSeer: Visualizing Learning Patterns in Massive Open Online Courses for Dropout Reasoning and Prediction

IEEE Conference on Visual Analytics Science and Technology (VAST), 2016

Author: Yuanzhe Chen, Qing Chen, Mingqian Zhao, Sebastien Boyer, Kalyan Veeramachaneni, Huamin Qu

Links: [PDF]

NetworkSeer: Visual Analysis for Social Network in MOOCs

Pacific Visualization Symposium (PacificVis), 2016

Author: Tongshuang Wu, Yuan Yao, Yuqing Duan, Xinzhi Fan, Huamin Qu

Links: [PDF]

PeakVizor: Visual Analytics of Peaks in Video Clickstreams from Massive Open Online Courses

IEEE Transactions on Visualization & Computer Graphics, 2016

Author: Qing Chen, Yuanzhe Chen, Dongyu Liu, Conglei Shi, Yingcai Wu, Huamin Qu

Links: [PDF]

A Narrative Visualization System for Peaks in Video Clickstreams from Massive Open Online Courses

HKUST TLE Mphil Thesis, 2016

Author: Chengjin Li (Supervisor: Huamin Qu)

VisMOOC: Visualizing Video Clickstream Data from Massive Open Online Courses

IEEE Pacific Visualization Symposium (PacificVis), 2015

Author: Conglei Shi, Siwei Fu, Qing Chen, Huamin Qu

Links: [PDF]

Visual Analytics of Data from MOOCs

IEEE Computer Graphics and Applications (CG&A) 2015

Author: Huamin Qu, Qing Chen

Links: [PDF]

Open Source


A visualization technique to analyze and visualize user's active behaviors for MOOCs by utilizing large-scale data.


A visualization technique to evaluates user’s problem-solving dynamics from both cognitive and non-cognitive perspectives.


Contact Us

haotian.li [at] connect dot ust dot hk, huamin [at] cse dot ust dot hk

AIS, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong