Boxuan Ma 馬　博軒

Assistant Professor 助理教授
Faculty of Arts and Science, Kyushu University 基幹教育院 九州大学
Fukuoka, Japan 福岡 日本

boxuan@artsci.kyushu-u.ac.jp
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Boxuan is currently working as an Assistant Professor in the Faculty of Arts and Science at Kyushu University. He is also an adjunct member of the School of Interdisciplinary Science and Innovation at Kyushu University. He received the B.S. and M.S. degrees from Southwest Jiaotong University, China, in 2014 and 2017, respectively, and the Ph.D. degree from Kyushu University, Fukuoka, Japan, in 2021. His main research interests include Artificial Intelligence in Education, Educational Data Mining, Learning Analytics, Human-Computer Interaction, and Recommender Systems. He is currently collaborating with the Human-Data Interaction (HDI) Lab at Kyushu University.

Research Topics



  1. Personalized Course Recommendation

    2. We conduct research on personalized course recommendation systems. This involved investigating course choice motivations, proposing and evaluating algorithms, designing and developing interfaces to provide better results that satisfy students’ different requirements.


    Tianyuan Yang, Baofeng Ren, Chenghao Gu, Boxuan Ma, Tianjia He and Shin'ichi Konomi (2025). Towards Better Course Recommendations: Integrating Multi-Perspective Meta-Paths and Knowledge Graphs. International Conference on Learning Analytics & Knowledge (LAK25). Dublin, Ireland. March 2025.


    Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi (2021). CourseQ: The Impact of Visual and Interactive Course Recommendation in University Environments. Research and Practice in Technology Enhanced Learning, 16, Article number: 18, Springer, Berlin/Heidelberg, June 30, 2021.


    Boxuan Ma, Min Lu, Yuta Taniguchi and Shin’ichi Konomi (2021). Investigating Course Choice Motivations in University Environments. Smart Learning Environment, 8, Article number: 31, Springer, Berlin/Heidelberg, November 27, 2021.


    Boxuan Ma, Min Lu, Yuta Taniguchi and Shin’ichi Konomi (2021). Exploration and Explanation: An Interactive Course Recommendation System for University Environments. Intelligent User Interfaces (IUI) Workshop on Exploratory Search and Interactive Data Analytics (ESIDA). CEUR-WS.org, Vol-2903, Online, April 13, 2021. pp.1-7.


    Boxuan Ma, Yuta Taniguchi, Shin’ichi Konomi (2020). Course Recommendation for University Environment. International Conference on Educational Data Mining (EDM 2020). pp.460-466.


  1. E-book Reading Behavior Analysis

    2. We analyze students' e-book reading behavior (e.g., how and why they jump back to the previous page, and what the differences in reading behavior patterns between traditional face-to-face and online classes during the pandemic) to explore how students interact with e-books and how their behaviors relate to their performance. Additionally, we design and develop smart interactions for E-book systems.


    Boxuan Ma, Min Lu, Li Chen and Masanori Yamada (2025). Connect E-book Content and Structure to Student Jump-Back Behavior. IEEE International Conference on Advanced Learning Technologies (ICALT 2025). Changhua, Taiwan. 2025.


    Min Lu, Boxuan Ma, Xuewang Geng and Masanori Yamada (2025). Enhancing E-Book Learning Dashboards with GPT-Assisted Page Grouping and Adaptive Navigation Link Visualization. International Conference on Learning Analytics & Knowledge (LAK25). Dublin, Ireland. March 2025.


    Boxuan Ma, Li Chen and Min Lu (2024). Personalized Navigation Recommendation for E-book Page Jump. Workshop on Predicting Performance Based on the Analysis of Reading Behavior (DC@LAK24), 2024.


    Boxuan Ma, Min Lu, Yuta Taniguchi and Shin’ichi Konomi (2021). Exploring Jump Back Behavior Patterns and Reasons in E-book System. Smart Learning Environments, 9, Article number: 2, Springer, Berlin/Heidelberg, January 4, 2022.


    Boxuan Ma, Min Lu and Shin’ichi Konomi (2021). Understanding Student Slide Reading Patterns During the Pandemic. International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2021).

  1. Language Learning Support

    2. We conduct research on personalized language learning support systems. For example, we develop cognitive diagnosis models and computerized adaptive test using deep learning to measure learners’ English proficiency. Besides that, we propose and evaluate deep learning methods that can accurately model learner language learning and forgetting processes for spaced repetition.


    Boxuan Ma, Sora Fukui, Yuji Ando and Shin'ichi Konomi (2025). Personalized Language Learning Using Spaced Repetition Scheduling. International Conference on Artificial Intelligence in Education (AIED 2025).


    Boxuan Ma, Sora Fukui, Yuji Ando and Shin'ichi Konomi (2024). Investigating Concept Definition and Skill Modeling for Cognitive Diagnosis in Language Learning. Journal of Educational Data Mining (JEDM).


    Boxuan Ma, Gayan Prasad Hettiarachchi, Sora Fukui and Yuji Ando (2023). Exploring the effectiveness of Vocabulary Proficiency Diagnosis Using Linguistic Concept and Skill Modeling. International Conference on Educational Data Mining (EDM 2023). pp. 149–159, Bengaluru, India. July 2023.


    Boxuan Ma, Gayan Prasad Hettiarachchi, Sora Fukui and Yuji Ando (2023). Each Encounter Counts: Modeling Language Learning and Forgetting. International Conference on Learning Analytics & Knowledge (LAK23). pp. 79–88, Arlington, USA. March 2023.


  1. LLM-based Programming Learning Assistant

    2. We conduct research on LLM-based programming learning assistants, focusing specifically on student experiences and interactions with ChatGPT in beginner-level Python programming courses. Our study explores the impact of these tools on student performance and the learning process. Our study also includes identifying best practices for their effective integration.


    Boxuan Ma, Liyuan Guo, Tianyuan Yang and Jihong Ding (2025). How Generative AI Impact Student Emotion and Engagement in Programming Tasks? International Conference on Artificial Intelligence in Education (AIED 2025)


    Boxuan Ma, Li Chen and Shin'ichi Konomi (2024). Enhancing Programming Education with ChatGPT: A Case Study on Student Perceptions and Interactions in a Python Course. International Conference on Artificial Intelligence in Education (AIED 2024), pp.113–126, 2024.


    Boxuan Ma, Li Chen and Shin'ichi Konomi (2024). Exploring Student Perception and Interaction Using ChatGPT In Programming Education. International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2024), 2024.


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