Data Insights Lab
資 料 洞 察
資 料 洞 察
The Data Insight Lab combines artificial intelligence, big data and machine learning for digital learning and educational data mining.
LAB members will train and cultivate themselves to have practical experience in the fields of big data computing and machine learning, develop the ability and research results to bridge academic research and industrial needs, and focus on program implementation and data analysis experience to gain valuable insights from data.
Hui-Chun Hung (Jimgo), Ph.D., is an associate professor at the Graduate Institute of Network Learning Technology, National Central University, Taiwan. His research interests include artificial intelligence in education, educational data mining, learning analytics, and visual data exploration. His recent work highlights the integration of generative AI, machine learning, and interactive dashboards to support adaptive learning and multi-modal behavior analysis in computer science and interdisciplinary education. He has received several teaching and research awards, including the College-level Excellent Teaching Award and the University-level Distinguished Teaching Award, as well as grants from the NSTC 2030 Cross-Generation Young Scholars Program. He is also actively involved in industry-academic collaboration and has led over 100 hands-on workshops in programming, data science, and data visualization.
The atmosphere in the laboratory is good, and the students get along well, looking out for one another.
The members of the laboratory are handsome, sunny and beautiful too XD.
Participate actively in domestic and international seminars to publish research papers.
We eat and drink happily. Work smart & Play hard!
We welcome master students, Ph D students who are interested in the laboratory to join us.
Data Visualization / Learinng Analytics Dashboard
Educational Data Mining
Information Education and Computational Thinking
Artificial Intelligence and Big Data in Education
Human-Computer interaction and User Behavior Analysis
Adaptive Programming Smart Learning System
Develop and construct an adaptive programming smart learning system for programming teaching. The system operation logs generated when students write programs are collected in real time through the course-specific server, and the data in the course management system is integrated, and the real-time quantitative presentation is displayed on the visual dashboard, so that teachers and students can understand the course progress and learning status.
Supporting Social Regulation Learning Analytics Dashboard
The classroom design of "learning by teaching" and the learning platform based on "Social Adjustment" have been introduced into the data analysis course, so that students can read the data of other outstanding peers, and achieve the purpose of social adjustment through the real-time update of the visual dashboard.
Cross-domain topic recommendation learning companion system based on knowledge graph
The purpose of this research is to develop and design a digital reading companion system for book reading. Through the design of digital learning companions, primary school students are guided to use them, and books are recommended according to the known map method of students' reading process, so as to help teachers understand students' reading situation and help improve students' reading performance.
An Adaptive Learning Process System for Physical Skills Mastery Learning
Aiming at the research on the application of proficient learning method, cross-platform mobile learning, mobile device equipment and learning history file system in badminton skill learning, a model of WISER's technology integration into enhanced learning is proposed, and real-time adaptability is explored in real-time learning situations in class and after class. The application of strategies to the learning of sports motor skills.
An Adaptive Learning Process System for Physical Skills Mastery Learning
Aiming at the research on the application of proficient learning method, cross-platform mobile learning, mobile device equipment and learning history file system in badminton skill learning, a model of WISER's technology integration into enhanced learning is proposed, and real-time adaptability is explored in real-time learning situations in class and after class. The application of strategies to the learning of sports motor skills.
Application of a GenAI-Assisted Question Generation Learning System in Elementary Social Studies
This study applies a GenAI-assisted question generation learning system in elementary social studies, fostering critical thinking and self-directed learning through both manual and automated question creation. By integrating gamification, the system enhances learning motivation and offers a new model for digital learning in social studies education.
Inquiry-Based Learning Companion System for Enhancing Graduate Students' Data Visualization Literacy
This study develops an intelligent inquiry-based learning companion system for visualization courses, utilizing GenAI technology to address resource limitations, reduce students' frustration, and foster critical thinking and problem-solving skills. The system provides real-time feedback to enhance the inquiry-based learning experience.
Application and Effectiveness Evaluation of a GenAI-Based Reading Learning Companion System for Elementary Students
This study develops an elementary reading learning companion system based on GenAI and the 4F dynamic review cycle theory. It utilizes a chatbot to assist students in reading reflection and interaction while analyzing learning progress through a dashboard. The system aims to enhance reading comprehension and examine its impact on reading motivation.
A Programming Learning Support System Based on Self-Explanation and AI Feedback : TaskInsighter
TaskInsighter is a generative AI learning system integrating the QLC strategy and self-explanation mechanism. Embedded in Python courses, the system generates open-ended questions based on students’ uploaded project code, guiding them to engage in self-explanation. After responding, students receive instant AI feedback and can refine their explanations through multiple interactive rounds to improve quality.
AI-Based English Learning Companion System
This system is a generative AI–driven English learning companion designed for elementary school contexts. It guides students through structured questioning and scaffolded dialogue to enhance their comprehension and language output. The system also includes a teacher-facing learning analytics dashboard that visualizes students’ interaction history to support real-time instructional adjustments.
Embodied Interactive System in English Speaking Proficiency
The purpose of this research is to develop and design an embodied interactive system for English language learning. Through the design of embodied learning companions, university students are guided in using them, and conversational topics are recommended based on the teacher’s instructional process. This approach helps teachers understand students' practice situations and enhances students’ English speaking proficiency.
Interactive robot learning design assisted by generative artificial intelligence
In elementary school science education, generative artificial intelligence was integrated into Scratch programming and LEGO SPIKE robotics kit courses to promote interactive learning and inquiry activities.
A generative artificial intelligence-based chatbot system was developed to assist experimental group students in more effectively mastering programming and LEGO robotics skills.
Reading Learning Companion System
Develop and build a reading learning companion system based on generative AI. Through the integration of the 4F reflective cycle, the system analyzes students' book discussion content in real time and automatically provides personalized immediate or summative feedback. The system also quantifies students' reading comprehension performance to help teachers and students grasp the learning process and enhance deep reading and comprehension abilities.