Boxuan Ma馬 博軒马博轩

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

boxuan@artsci.kyushu-u.ac.jp
Google Scholar   Github

2020
2021
2025
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.馬博軒は、九州大学基幹教育院の助教です。また、九州大学共創学部の兼任教員でもあります。 2014年と2017年に中国の西南交通大学で学士号と修士号を取得し、2021年に九州大学で博士号を取得しました。 主な研究分野は、教育における人工知能、教育データマイニング、学習分析、ヒューマンコンピュータインタラクション、および推薦システムです。現在、九州大学のHuman-Data Interaction (HDI)研究室と共同研究を行っています。马博轩目前在九州大学基幹教育院担任助理教授，同时也是九州大学共创学部的兼任教员。 他于2014年和2017年在中国西南交通大学分别获得学士学位和硕士学位，并于2021年在日本福冈的九州大学获得博士学位。 他的主要研究方向包括教育人工智能、教育数据挖掘、学习分析、人机交互以及推荐系统。目前，他正与九州大学的Human-Data Interaction (HDI)实验室进行合作研究。

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Research Topics研究テーマ研究方向



  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.
    ▶ Related Papers (5)関連論文 (5)相关論文 (5)

    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).


    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.


    Boxuan Ma, Min Lu, Yuta Taniguchi and Shin'ichi Konomi (2021). Investigating Course Choice Motivations in University Environments. Smart Learning Environments.


    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).


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

  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.
    ▶ Related Papers (6)関連論文 (6)相关論文 (6)

    Boxuan Ma, Li Chen, Xuewang Geng and Masanori Yamada (2026). Understanding Study Approaches in E-Book Logs and Their Relation to Metacognition and Performance. International Conference on Learning Analytics & Knowledge (LAK26).


    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).


    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).


    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).


    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.


    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.
    ▶ Related Papers (5)関連論文 (5)相关論文 (5)

    Boxuan Ma, Sora Fukui, Yuji Ando and Shin'ichi Konomi (2026). Integrating Forgetting Behavior and Linguistic Features in Language Learning Models. ACM Transactions on Knowledge Discovery from Data (TKDD).


    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).


    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).

  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.
    ▶ Related Papers (4)関連論文 (4)相关論文 (4)

    Huiyong Li and Boxuan Ma (2025). CodeRunner Agent: Integrating AI Feedback and Self-Regulated Learning to Support Programming Education. International Conference on Computers in Education (ICCE 2025)


    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).


    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).

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