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 educational data mining, learning analytics, human-computer interaction, and recommender systems.

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.


    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. Fourth 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. Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020), International Educational Data Mining Society (IEDMS), Worcester. 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, Li Chen and Min Lu (2024). Personalized Navigation Recommendation for E-book Page Jump. The 6th 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. The 18th 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 (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. Proceedings of the 16th 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. Proceedings of the 13th 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, Li Chen and Shin'ichi Konomi (2024). Enhancing Programming Education with ChatGPT: A Case Study on Student Perceptions and Interactions in a Python Course. The 25th 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. The 21st International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2024), 2024.


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