Updated on 2025/06/29

Information

 

写真a

 
MA BOXUAN
 
Organization
Faculty of Arts and Science Division for Theoretical Natural Science Assistant Professor
School of Interdisciplinary Science and Innovation (Concurrent)
Title
Assistant Professor
Contact information
メールアドレス
Tel
0928026016
Profile
Boxuan is currently working as an Assistant Professor in the Faculty of Arts and Science at Kyushu University. His main research interests include educational data mining, learning analytics, human-computer interaction, and recommender systems.
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Research Areas

  • Informatics / Learning support system

  • Informatics / Human interface and interaction

Degree

  • Doctor of Engineering

Research History

  • Kyushu University Faculty of Arts and Science Assistant Professor 

    2023.4 - Present

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Education

  • Kyushu University    

    2018.10 - 2021.9

Research Interests・Research Keywords

  • Research theme: 学習支援システム

    Keyword: 学習支援システム

    Research period: 2024

  • Research theme: 言語学習支援

    Keyword: 言語学習支援

    Research period: 2024

  • Research theme: 知識追跡

    Keyword: 知識追跡

    Research period: 2024

  • Research theme: 推薦システム

    Keyword: 推薦システム

    Research period: 2024

  • Research theme: 学習診断

    Keyword: 学習診断

    Research period: 2024

  • Research theme: 利用者インタフェース

    Keyword: 利用者インタフェース

    Research period: 2024

  • Research theme: ラーニングアナリティクス

    Keyword: ラーニングアナリティクス

    Research period: 2024

Awards

  • Best paper award

    2024.10   21th International Conference on Cognition and Exploratory Learning in Digital Age   LEVERAGING CHATGPT FOR AUTOMATED KNOWLEDGE CONCEPT GENERATION

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    Award type:Award from international society, conference, symposium, etc. 

Papers

  • Towards Better Course Recommendations: Integrating Multi-Perspective Meta-Paths and Knowledge Graphs. Reviewed

    Tianyuan Yang, Baofeng Ren, Chenghao Gu, Boxuan Ma, Tianjia He, Shin'ichi Konomi

    LAK   137 - 147   2025   ISBN:979-8-4007-0701-8

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    Publishing type:Research paper (international conference proceedings)   Publisher:15th International Conference on Learning Analytics and Knowledge Lak 2025  

    Course recommender systems demonstrate their potential in assisting students with course selection and effectively alleviating the problem of information overload. Current course recommender systems focus predominantly on collaborative information and fail to consider the multi-perspective information and the bi-directional relationship between students and courses. This paper introduces a novel Multi-perspective Aware Explainable Course Recommendation model (MAECR) that leverages knowledge graphs and multi-perspective meta-paths to enhance both the accuracy and explainability of course recommendations. By the dual-side modeling from both the student and the course for each meta-path, MAECR can identify and understand the interests and needs of students in each course, as well as evaluate the attractiveness and suitability of the courses for individual students. Following the dual-side modeling for each meta-path, we aggregate multi-perspective meta-paths of each student and course using a carefully designed attention mechanism. The attention weights generated by this mechanism serve as explanations for the recommendation results, representing the preference score for each perspective. MAECR thus provides personalized and explainable recommendations. Comprehensive experiments are implemented to demonstrate the effectiveness and improved interpretability of the proposed model.

    DOI: 10.1145/3706468.3706486

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    Other Link: https://dblp.uni-trier.de/db/conf/lak/lak2025.html#YangRGMHK25

  • Investigating Concept Definition and Skill Modeling for Cognitive Diagnosis in Language Learning Invited Reviewed

    Boxuan Ma, Sora Fukui, Yuji Ando, Shin’ichi Konomi

    Journal of Educational Data Mining (JEDM)   16 ( 1 )   303 - 329   2024.6   ISSN:2157-2100

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    Authorship:Lead author, Last author, Corresponding author   Publishing type:Research paper (scientific journal)   Publisher:Journal of Educational Data Mining  

    Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge concepts and linguistic skills are hard to define, and diagnosis must be well-designed. Traditional approaches are widely applied for modeling knowledge in science or mathematics, where skills or knowledge concepts are easy to associate with each item. However, only a few works focus on defining knowledge concepts and skills using linguistic characteristics for language knowledge proficiency diagnosis. In addressing this, we propose a framework for language proficiency diagnosis based on neural networks. Specifically, we propose a series of methods based on our framework that uses different linguistic features to define skills and knowledge concepts in the context of the language learning task. Experimental results on a real-world second-language learning dataset demonstrate the effectiveness and interpretability of our framework. We also provide empirical evidence with comprehensive experiments and analysis to prove that our knowledge concept and skill definitions are reasonable and critical to the performance of our model.

    DOI: 10.5281/zenodo.10948071

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  • Exploring the Effectiveness of Vocabulary Proficiency Diagnosis Using Linguistic Concept and Skill Modeling Reviewed

    Boxuan Ma, Gayan, Prasad Hettiarachchi, Sora Fukui, Yuji Ando

    Proceedings of the 16th International Conference on Educational Data Mining   149 - 159   2023.7

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    Language:English   Publishing type:Research paper (other academic)  

  • Exploring the Effectiveness of Vocabulary Proficiency Diagnosis Using Linguistic Concept and Skill Modeling Reviewed

    Boxuan Ma, Gayan, Prasad Hettiarachchi, Sora Fukui, Yuji Ando

    Proceedings of the 16th International Conference on Educational Data Mining   149 - 159   2023.7

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    Authorship:Lead author, Last author, Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)  

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  • Each Encounter Counts: Modeling Language Learning and Forgetting Reviewed

    Boxuan Ma, Gayan Prasad Hettiarachchi, Sora Fukui, Yuji Ando

    ACM International Conference Proceeding Series   79 - 88   2023.3   ISBN:9781450398657

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    Language:Others   Publishing type:Research paper (other academic)  

    Language learning applications usually estimate the learner's language knowledge over time to provide personalized practice content for each learner at the optimal timing. However, accurately predicting language knowledge or linguistic skills is much more challenging than math or science knowledge, as many language tasks involve memorization and retrieval. Learners must memorize a large number of words and meanings, which are prone to be forgotten without practice. Although a few studies consider forgetting when modeling learners' language knowledge, they tend to apply traditional models, consider only partial information about forgetting, and ignore linguistic features that may significantly influence learning and forgetting. This paper focuses on modeling and predicting learners' knowledge by considering their forgetting behavior and linguistic features in language learning. Specifically, we first explore the existence of forgetting behavior and cross-effects in real-world language learning datasets through empirical studies. Based on these, we propose a model for predicting the probability of recalling a word given a learner's practice history. The model incorporates key information related to forgetting, question formats, and semantic similarities between words using the attention mechanism. Experiments on two real-world datasets show that the proposed model improves performance compared to baselines. Moreover, the results indicate that combining multiple types of forgetting information and item format improves performance. In addition, we find that incorporating semantic features, such as word embeddings, to model similarities between words in a learner's practice history and their effects on memory also improves the model.

    DOI: 10.1145/3576050.3576062

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  • Exploring jump back behavior patterns and reasons in e-book system Reviewed

    Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi

    Smart Learning Environments   9 ( 1 )   2   2022.12   eISSN:2196-7091

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    Language:Others   Publishing type:Research paper (scientific journal)   Publisher:Smart Learning Environments  

    With the increasing use of digital learning materials in higher education, the accumulated operational log data provide a unique opportunity to analyzing student learning behaviors and their effects on student learning performance to understand how students learn with e-books. Among the students’ reading behaviors interacting with e-book systems, we find that jump-back is a frequent and informative behavior type. In this paper, we aim to understand the student’s intention for a jump-back using user learning log data on the e-book materials of a course in our university. We at first formally define the “jump-back” behaviors that can be detected from the click event stream of slide reading and then systematically study the behaviors from different perspectives on the e-book event stream data. Finally, by sampling 22 learning materials, we identify six reading activity patterns that can explain jump backs. Our analysis provides an approach to enriching the understanding of e-book learning behaviors and informs design implications for e-book systems.

    DOI: 10.1186/s40561-021-00183-6

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  • CourseQ: the impact of visual and interactive course recommendation in university environments Reviewed

    Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi

    Research and Practice in Technology Enhanced Learning   16 ( 1 )   2021.12

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    Language:Others   Publishing type:Research paper (scientific journal)  

    The abundance of courses available in a university often overwhelms students as they must select courses that are relevant to their academic interests and satisfy their requirements. A large number of existing studies in course recommendation systems focus on the accuracy of prediction to show students the most relevant courses with little consideration on interactivity and user perception. However, recent work has highlighted the importance of user-perceived aspects of recommendation systems, such as transparency, controllability, and user satisfaction. This paper introduces CourseQ, an interactive course recommendation system that allows students to explore courses by using a novel visual interface so as to improve transparency and user satisfaction of course recommendations. We describe the design concepts, interactions, and algorithm of the proposed system. A within-subject user study (N=32) was conducted to evaluate our system compared to a baseline interface without the proposed interactive visualization. The evaluation results show that our system improves many user-centric metrics including user acceptance and understanding of the recommendation results. Furthermore, our analysis of user interaction behaviors in the system indicates that CourseQ could help different users with their course-seeking tasks. Our results and discussions highlight the impact of visual and interactive features in course recommendation systems and inform the design of future recommendation systems for higher education.

    DOI: 10.1186/s41039-021-00167-7

  • Investigating course choice motivations in university environments Reviewed

    Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi

    Smart Learning Environments   8 ( 1 )   2021.12

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    Language:Others   Publishing type:Research paper (scientific journal)  

    Recommendation systems need a deeper understanding of users and their motivations to improve recommendation quality and provide more personalized suggestions. This is especially true in the education domain, the more about the student is known, the more useful recommendations can be made. However, although many studies on the course recommendation exist, studies on the students’ course selection motivations in universities are limited. This study investigates the factors that contribute to students’ choice when selecting courses in universities to better understand student perceptions, attitudes, and needs and leverage data-driven approaches for recommending and explaining the recommendations in university environments. A qualitative interview for university students (N = 10) comprised of open-ended questions as well as a questionnaire for students (N = 81) was conducted, aiming to investigate the main reasons behind their choices. The results of this study show that students highly value the course contents and the benefits of the course towards their future careers. Furthermore, students are influenced by other reasons such as the possibility of obtaining a higher grade, the popularity of professors, and recommendations from peers. Next, we extract the main categories of students’ motivations and analyzed the questionnaire data by employing statistical analysis methods as well as the k-means clustering algorithm to identify different types of students in terms of course selection. Based on our findings, we discuss implications for designing more personalized course recommendation systems.

    DOI: 10.1186/s40561-021-00177-4

  • Course Recommendation for University Environment. Reviewed

    Boxuan Ma, Yuta Taniguchi, Shin'ichi Konomi

    Proceedings of the 13th International Conference on Educational Data Mining(EDM)   2020.7

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    Language:Others   Publishing type:Research paper (other academic)  

  • A Framework for Constructing Concept Maps from E-Books Using Large Language Models: Challenges and Future Directions Reviewed

    Boxuan Ma, Li Chen

    The 7th Workshop on Predicting Performance Based on the Analysis of Reading Behavior (DC@LAK25)   2025

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  • Enhancing E-Book Learning Dashboards with GPT-Assisted Page Grouping and Adaptive Navigation Link Visualization Reviewed

    Min Lu, Boxuan Ma, Xuewang Geng, Masanori Yamada

    International Conference on Learning Analytics & Knowledge (LAK25), 2025.   2025

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  • Design of AI-Powered Tool for Self-Regulation Support in Programming Education Reviewed

    Huiyong Li, Boxuan Ma

    CHI 2025 Workshop: Augmented Educators and AI: Shaping the Future of Human-AI Collaboration in Learning (CHI2025)   2025

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    Authorship:Last author, Corresponding author  

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  • Coordination of Speaking Opportunities in Virtual Reality: Analyzing Interaction Dynamics and Context-Aware Strategies Reviewed

    Jiadong Chen, Chenghao Gu, Jiayi Zhang, Zhankun Liu, Boxuan Ma, Shin‘ichi Konomi

    Applied Sciences   14 ( 24 )   2024.12   eISSN:2076-3417

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    Publishing type:Research paper (scientific journal)   Publisher:Applied Sciences Switzerland  

    This study explores the factors influencing turn-taking coordination in virtual reality (VR) environments, with a focus on identifying key interaction dynamics that affect the ease of gaining speaking opportunities. By analyzing VR interaction data through logistic regression and clustering, we identify significant variables impacting turn-taking success and categorize typical interaction states that present unique coordination challenges. The findings reveal that features related to interaction proactivity, individual status, and communication quality significantly impact turn-taking outcomes. Furthermore, clustering analysis identifies five primary interaction contexts: high competition, intense interaction, prolonged single turn, high-status role, and low activity, each with unique turn-taking coordination requirements. This work provides insights into enhancing turn-taking support systems in VR, emphasizing contextually adaptive feedback to reduce speaking overlap and turn-taking failures, thereby improving overall interaction flow in immersive environments.

    DOI: 10.3390/app142412071

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  • Making Course Recommendation Explainable: A Knowledge Entity-Aware Model using Deep Learning Reviewed

    Tianyuan Yang, Baofeng Ren, Boxuan Ma, Md Akib Zabed Khan, Tianjia He, Shin'Ichi Konomi

    International Conference on Educational Data Mining (EDM 2024), 2024.   2024.7

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    Language:Others  

  • A Survey on Explainable Course Recommendation Systems Invited Reviewed

    Boxuan Ma, Tianyuan Yang, Baofeng Ren

    International Conference on Distributed, Ambient, and Pervasive Interactions (DAPI 2024), Held as Part of HCI International 2024, 2024.   2024.7

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    Language:Others  

  • Enhancing Programming Education with ChatGPT: A Case Study on Student Perceptions and Interactions in a Python Course Reviewed

    Boxuan Ma, Li Chen, Shin'ichi Konomi

    International Conference on Artificial Intelligence in Education (AIED 2024), 2024   2024.7

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  • How Do Strategies for Using ChatGPT Affect Knowledge Comprehension? Reviewed

    Li Chen, Gen Li, Boxuan Ma, Cheng Tang, Fumiya Okubo, Atsushi Shimada

    International Conference on Artificial Intelligence in Education (AIED 2024), 2024.   2024.7

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  • Enhancing Programming Education with ChatGPT: A Case Study on Student Perceptions and Interactions in a Python Course Reviewed

    Boxuan Ma, Li Chen, Shin'ichi Konomi

    International Conference on Artificial Intelligence in Education (AIED 2024), 2024   2024.7

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    Authorship:Lead author, Last author, Corresponding author  

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  • Making Course Recommendation Explainable: A Knowledge Entity-Aware Model using Deep Learning Reviewed

    Tianyuan Yang, Baofeng Ren, Boxuan Ma, Md Akib Zabed Khan, Tianjia He, Shin'Ichi Konomi

    International Conference on Educational Data Mining (EDM 2024), 2024.   2024.7

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    Publishing type:Research paper (international conference proceedings)   Publisher:International Educational Data Mining Society  

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    Other Link: https://dblp.uni-trier.de/rec/conf/edm/2024

  • How Do Strategies for Using ChatGPT Affect Knowledge Comprehension? Reviewed

    Li Chen, Gen Li, Boxuan Ma, Cheng Tang, Fumiya Okubo, Atsushi Shimada

    International Conference on Artificial Intelligence in Education (AIED 2024), 2024.   2024.7

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  • Investigating Concept Definition and Skill Modeling for Cognitive Diagnosis in Language Learning Invited Reviewed

    Boxuan Ma, Sora Fukui, Yuji Ando, Shin’ichi Konomi

    Journal of Educational Data Mining (JEDM)   2024.6

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    Language:Others  

  • Personalized Navigation Recommendation for E-book Page Jump Reviewed

    Boxuan Ma, Li Chen, Min Lu

    The 6th Workshop on Predicting Performance Based on the Analysis of Reading Behavior (DC@LAK24), 2024.   2024.3

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    Language:English  

  • Personalized Navigation Recommendation for E-book Page Jump Reviewed

    Boxuan Ma, Li Chen, Min Lu

    The 6th Workshop on Predicting Performance Based on the Analysis of Reading Behavior (DC@LAK24), 2024.   32 - 41   2024.3

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    Authorship:Lead author, Last author, Corresponding author   Publishing type:Research paper (international conference proceedings)   Publisher:CEUR-WS.org  

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    Other Link: https://dblp.uni-trier.de/rec/conf/lak/2024w

  • Personalized Navigation Recommendation for E-book Page Jump

    Ma B., Chen L., Lu M.

    CEUR Workshop Proceedings   3667   32 - 41   2024   ISSN:16130073

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    Publisher:CEUR Workshop Proceedings  

    As the utilization of digital learning materials continues to rise in higher education, the accumulated operational log data provide a unique opportunity to analyze student reading behaviors. Previous works on reading behaviors for e-books have identified jump-back as frequent student behavior, which refers to students returning to previous pages to reflect on them during the reading. However, the lack of navigation in e-book systems makes finding the right page at once challenging. Students usually need to try several times to find the correct page, which indicates the strong demand for personalized navigation recommendations. This work aims to help the student alleviate this problem by recommending the right page for a jump-back. Specifically, we propose a model for personalized navigation recommendations based on neural networks. A two-phase experiment is conducted to evaluate the proposed model, and the experimental result on real-world datasets validates the feasibility and effectiveness of the proposed method.

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  • How Do Strategies for Using ChatGPT Affect Knowledge Comprehension?

    Chen L., Li G., Ma B., Tang C., Okubo F., Shimada A.

    Communications in Computer and Information Science   2150 CCIS   151 - 162   2024   ISSN:18650929 ISBN:9783031643149

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    Publisher:Communications in Computer and Information Science  

    This study investigates the effects of generative AI on the knowledge comprehension of university students, focusing on the use of ChatGPT strategies. Data from 81 junior students who used the ChatGPT worksheet were collected and analyzed. Path analysis revealed complex interactions between ChatGPT strategy use, e-book reading behaviors, and students’ prior perceived understanding of concepts. Students’ prior perceived understanding and reading behaviors indirectly affected their final scores, mediated by the ChatGPT strategy use. The mediation effects indicated that reading behaviors significantly influenced final scores through ChatGPT strategies, indicating the importance of the interaction with learning materials. Further regression analysis identified the specific ChatGPT strategy related to verifying and comparing information sources as significantly influenced by reading behaviors and directly affecting students’ final scores. The findings provide implications for practical strategic guidance for integrating ChatGPT in education.

    DOI: 10.1007/978-3-031-64315-6_12

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  • Enhancing Programming Education with ChatGPT: A Case Study on Student Perceptions and Interactions in a Python Course

    Ma B., Chen L., Konomi S.

    Communications in Computer and Information Science   2150 CCIS   113 - 126   2024   ISSN:18650929 ISBN:9783031643149

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    Publisher:Communications in Computer and Information Science  

    The integration of ChatGPT as a supportive tool in education, notably in programming courses, addresses the unique challenges of programming education by providing assistance with debugging, code generation, and explanations. Despite existing research validating ChatGPT’s effectiveness, its application in university-level programming education and a detailed understanding of student interactions and perspectives remain limited. This paper explores ChatGPT’s impact on learning in a Python programming course tailored for first-year students over eight weeks. By analyzing responses from surveys, open-ended questions, and student-ChatGPT dialog data, we aim to provide a comprehensive view of ChatGPT’s utility and identify both its advantages and limitations as perceived by students. Our study uncovers a generally positive reception toward ChatGPT and offers insights into its role in enhancing the programming education experience. These findings contribute to the broader discourse on AI’s potential in education, suggesting paths for future research and application.

    DOI: 10.1007/978-3-031-64315-6_9

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  • A Survey on Explainable Course Recommendation Systems Invited Reviewed

    Boxuan Ma, Tianyuan Yang, Baofeng Ren

    International Conference on Distributed, Ambient, and Pervasive Interactions (DAPI 2024), Held as Part of HCI International 2024, 2024.   14719   273 - 287   2024   ISSN:0302-9743 ISBN:978-3-031-60011-1 eISSN:1611-3349

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    Authorship:Lead author, Last author, Corresponding author   Publishing type:Research paper (international conference proceedings)   Publisher:Springer  

    An emerging challenge in course recommendation systems is the need to explain clearly to students the rationale behind specific course recommendations. Consequently, recent research has transitioned from focusing primarily on the accuracy of these systems to prioritizing user-centric qualities, such as transparency and justification. This shift has led to an increased emphasis on methods that provide clear, understandable explanations for their recommendations. In response to this trend, our paper introduces an explainable recommendation framework. Utilizing this framework, we analyze existing course recommendation systems and explore the emerging research challenges and future prospects for explainable course recommendation systems.

    DOI: 10.1007/978-3-031-60012-8_17

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    Other Link: https://dblp.uni-trier.de/db/conf/hci/hci2024-36.html#MaYR24

  • A Three-Step Knowledge Graph Approach Using LLMs In Collaborative Problem Solving-based Stem Education Reviewed

    Li Chen, Gen Li, Boxuan Ma, Cheng Tang, Masanori Yamada

    International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2024)   51 - 58   2024

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  • Leveraging ChatGPT For Automated Knowledge Concept Generation Reviewed

    Tianyuan Yang, Baofeng Ren, Chenghao Gu, Boxuan Ma, Shin'ichi Konomi

    International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2024)   75 - 82   2024

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  • Exploring Student Perception and Interaction Using ChatGPT In Programming Education Reviewed

    Boxuan Ma, Li Chen, Shin'ichi Konomi

    International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2024)   35 - 42   2024

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  • EEMR: An Emotion-Enhancing Hybrid Recommendation Mechanism for Music Playlists

    Xu Feike, Ma Boxuan, Konomi Shin’ichi

    Special Interest Group on Web Intelligence and Interaction   20 ( 0 )   79 - 86   2024   eISSN:27582922

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    Language:Japanese   Publisher:Special Interest Group on Web Intelligence and Interaction  

    <p>Affective states play a crucial role in music, as music can influence both current emotions and long-term moods. We propose a novel <i>emotion-enhancing</i> hybrid music recommendation (EEMR) mechanism that finds the suitable criteria in selecting the best music playlist for improving user’s emotion by combining two recommendation techniques, i.e., content-based filtering and context-aware approach. This mechanism generates playlists that align with the user’s preferences and current emotion, while also supporting gradual improvement of the user’s emotion over time.</p>

    DOI: 10.57413/wii.20.0_79

    CiNii Research

  • Boosting Course Recommendation Explainability: A Knowledge Entity Aware Model Using Deep Learning.

    Tianyuan Yang, Baofeng Ren, Boxuan Ma, Tianjia He, Chenghao Gu, Shin'ichi Konomi

    ICCE   2024

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    Publishing type:Research paper (international conference proceedings)  

    DOI: 10.58459/icce.2024.4862

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    Other Link: https://dblp.uni-trier.de/db/conf/icce/icce2024.html#YangRMHGK24

  • Boosting Course Recommendation Explainability: A Knowledge Entity Aware Model Using Deep Learning

    Yang, TY; Ren, BF; Ma, BX; He, TJ; Gu, CH; Konoml, S

    32ND INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION CONFERENCE PROCEEDINGS, ICCE 2024, VOL I   360 - 366   2024   ISSN:3078-4360 ISBN:978-626-968-904-0

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  • Format-Aware Item Response Theory for Predicting Vocabulary Proficiency Reviewed

    Boxuan Ma, Gayan Prasad Hettiarachchi, Yuji Ando

    Proceedings of the 15th International Conference on Educational Data Mining   695 - 700   2022.7

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    Language:English   Publishing type:Research paper (other academic)  

  • Format-Aware Item Response Theory for Predicting Vocabulary Proficiency Reviewed

    Boxuan Ma, Gayan Prasad Hettiarachchi, Yuji Ando

    Proceedings of the 15th International Conference on Educational Data Mining   695 - 700   2022.7

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    Authorship:Lead author, Last author, Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)  

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  • UNDERSTANDING STUDENT SLIDE READING PATTERNS DURING THE PANDEMIC Reviewed

    Boxuan Ma, Min Lu, Shin'ichi Konomi

    18th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2021   87 - 94   2021.10

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    Language:Others   Publishing type:Research paper (other academic)  

    The COVID-19 pandemic has resulted in school closures all across the world, and lots of students have shifted from conventional classrooms to online learning. With the help of ICT technologies nowadays, learning online can be more effective in a number of ways. However, most of the online learning environments without instructors' attention may result in different learning patterns compared to the traditional face-to-face classroom. In this paper, we aimed at detecting the slide reading behaviors of the students by analyzing operational event logs from a digital textbook reader for a lecture offered in our university. We compared reading patterns between traditional face-to-face lectures and hybrid online lectures, our results show that online lectures lead to more off-task behaviors. Our analysis provides a rich understanding of e-book reading and informs design implications for online learning during the pandemic. The findings can also be used to improve the instruction designs and learning strategies.

  • Exploration and Explanation: An Interactive Course Recommendation System for University Environments Reviewed

    Boxuan Ma, Min Lu, Yuta Taniguchi, Shin'ichi Konomi

    CEUR Workshop Proceedings   2903   2021.4

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    Language:Others   Publishing type:Research paper (other academic)  

    The abundance of courses available in university and the highly personalized curriculum is often overwhelming for students who must select courses relevant to their academic interests. A large body of research in course recommendation systems focuses on optimizing prediction and improving accuracy. However, those systems usually afford little or no user interaction, and little is known about the influence of user-perceived aspects for course recommendations, such as transparency, controllability, and user satisfaction. In this paper, we argue that involving students in the course recommendation process is important, and we present an interactive course recommendation system that provides explanations and allows students to explore courses in a personalized way. A within-subject user study was conducted to evaluate our system and the results show a significant improvement in many user-centric metrics.

  • Exploring the Design Space for Explainable Course Recommendation Systems in University Environments Reviewed

    Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi

    Companion Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK20)   2020.3

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  • Understanding Jump Back Behaviors in E-book System Reviewed

    Boxuan Ma, Jiadong Chen, Chenhao Li, Likun Liu, Min Lu, Yuta Taniguchi, Shin’ichi Konomi

    Companion Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK20)   2020.3

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  • Design of an elective course recommendation system for university environment Reviewed

    Boxuan Ma

    EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining   699 - 701   2019.7

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    Course recommendation system is a useful tool that not only helps the students who have no sufficient experience to decide what they should study, but also leverages their full performance if they could study what they like or are interested in. Different from MOOCs, the selection and recommendation for hybrid learning environments such as university are relatively difficult. Students who enrolled in the same course may have completely different purposes and different interest. Employing the enrollment record data from Kyushu University, we conduct a systematic investigation on the course-taking pattern for recommendation. We then discuss the challenges to recommend suitable courses in university and propose a preliminary approach to address the challenges by designing a course recommendation mechanism based on association rule of previous course-taking pattern together with student interest.

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Presentations

  • Making Course Recommender Systems Interpretable: A Feature-aware Deep Learning-based Approach

    Tianyuan Yang, Baofeng Ren, Boxuan Ma, Shin’ichi Konomi

    The 86th National Convention of IPSJ, 2024.  2024.3 

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    Event date: 2024.3

    Language:English  

    Country:Japan  

  • Design a Course Recommendation System Based on Association Rule for Hybrid Learning Environments

    Boxuan Ma, Yuta Taniguchi, Shin’ichi Konomi

    Hinokuni-Land of Fire Information Processing Symposium  2019.3 

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    Language:Others  

    Country:Other  

  • Learning path recommendation in university environments based on sequence mining

    Boxuan Ma, Yuta Taniguchi, Shin’ichi Konomi

    The 81st National Convention of IPSJ  2019.2 

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    Country:Other  

  • Comparative Analysis of Adaptive Learning Path Recommendation Algorithms

    Boxuan Ma, Yuta Taniguchi, Shin’ichi Konomi

    Joint Conference of Electrical, Electronics and Information Engineers in Kyushu  2018.9 

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    Language:Others  

    Country:Other  

  • 言語の習得と忘却のモデル化

    馬 博軒

    2024年 情報科学技術フォーラム(FIT) トップコンファレンス6-2 障害者支援と教育学習支援情報システム  2024.9 

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    Presentation type:Oral presentation (invited, special)  

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MISC

  • Making Course Recommender Systems Interpretable: A Feature-aware Deep Learning-based Approach

    Tianyuan Yang, Baofeng Ren, Boxuan Ma, Shin’ichi Konomi

    The 86th National Convention of IPSJ, 2024.   2024.3

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  • Learning path recommendation in university environments based on sequence mining

    馬, 博軒, 谷口, 雄太, 木實, 新一

    第81回全国大会講演論文集   2019.2

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    Language:English  

    Learning path recommendation system efficiently guides learners by constructing appropriate learning sequences from recommended learning materials to reach their goals. However, supporting active learning in the learning path recommendation systems for university environments is different from conventional mechanisms for recommending relevant online courses such as MOOCs. This paper analyzed different learning path patterns of students at Kyushu University and discussed the challenges to recommend appropriate learning sequence in university learning environments. Then we proposed an approach to address the challenges by designing a learning path recommendation mechanism based on sequence mining.

  • アダプティブなラーニングバス推薦アルゴリズムに関する比較解析

    馬 博軒, 谷口 雄太, 木實 新一

    電気関係学会九州支部連合大会講演論文集   2018.9

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    Language:Japanese  

    Comparative Analysis of Adaptive Learning Path Recommendation Algorithms

    DOI: 10.11527/jceeek.2018.0_258

Professional Memberships

  • The Institute of Electrical and Electronics Engineers (IEEE)

    2023 - Present

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  • International Educational Data Mining Society (IEDMS)

    2020 - Present

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  • Association for Computing Machinery (ACM)

    2020 - Present

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  • Society for Learning Analytics Research (SoLAR)

    2020 - Present

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  • Association for Computing Machinery (ACM)

  • International Educational Data Mining Society (IEDMS)

  • Society for Learning Analytics Research (SoLAR)

  • The Institute of Electrical and Electronics Engineers (IEEE)

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Committee Memberships

  • 1st International Conference on Learning Evidence and Analytics (ICLEA 2025)   Local Organizing Committee   Foreign country

       

Academic Activities

  • Neurocomputing

    Role(s): Peer review

    2025 - Present

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  • The 18th International Conference on Educational Data Mining

    Role(s): Peer review

    2025

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  • PC member International contribution

    The 17th International Conference on Educational Data Mining  ( UnitedStatesofAmerica ) 2024.7

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    Type:Competition, symposium, etc. 

  • The 17th International Conference on Educational Data Mining

    2024 - Present

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    Type:Academic society, research group, etc. 

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  • Journal of Educational Data Mining (JEDM)

    Role(s): Peer review

    2024 - Present

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  • Transactions on Knowledge and Data Engineering (TKDE)

    Role(s): Peer review

    2024 - Present

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  • Information Processing & Management (IPM)

    Role(s): Peer review

    2024 - Present

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  • Research and Practice in Technology Enhanced Learning (RPTEL)

    Role(s): Peer review

    2023 - Present

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  • Screening of academic papers

    Role(s): Peer review

    2023

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    Type:Peer review 

    Number of peer-reviewed articles in foreign language journals:6

    Proceedings of International Conference Number of peer-reviewed papers:4

  • International Journal of Artificial Intelligence in Education (IJAIED)

    Role(s): Peer review

    2021 - Present

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    Type:Peer review 

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  • The 25th IEEE International Conference on Advanced Learning Technologies (ICALT 2025)

    Role(s): Peer review

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  • Humanities and Social Sciences Communications

    Role(s): Peer review

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  • Connection Science

    Role(s): Peer review

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  • Big Data Mining and Analytics (BDMA)

    Role(s): Peer review

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  • 26th International Conference on Artificial Intelligence in Education

    Role(s): Peer review

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  • 1st International Conference on Learning Evidence and Analytics (ICLEA 2025)

    Role(s): Planning, management, etc., Peer review

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Research Projects

  • デジタル社会における理工系人材育成を目的としたICT 活用型カリキュラム・授業デ ザインの開発と評価

    2025.4 - 2026.3

    共同研究 

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    Authorship:Research collaborator  Grant type:Joint research

  • A Framework for Fast, Accurate, and Explainable Computerized Adaptive Language Test

    Grant number:24K20903  2024 - 2026

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Early-Career Scientists

    馬 博軒

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    Authorship:Principal investigator  Grant type:Scientific research funding

    This research aims to develop an adaptive language assessment system through a computerized adaptive test framework. It integrates a cognitive diagnosis model to estimate learners’ ability and an adaptive question selection algorithm that considers the quality and diversity of questions.

    CiNii Research

  • 英語学習支援システムに関する研究

    2023.6 - 2026.5

    共同研究 

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    Authorship:Principal investigator 

  • Exploring Forgetting Behavior From Learning Data for Enhancing Knowledge Tracing

    2023 - 2024

    数理・データサイエンスに関する教育・研究支援プログラム

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    Authorship:Principal investigator  Grant type:On-campus funds, funds, etc.

  • Distributed Cooperative Learning Analytics for Developing Communities

    Grant number:20H00622  2020.4 - 2025.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

    木實 新一, 瀬崎 薫, 畑埜 晃平, 馬 博軒, 西山 勇毅

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    Grant type:Scientific research funding

    先進国においてはビッグデータやAI技術を活用した高度な学習支援システムが急速に発展しつつありますが、開発途上地域では状況が異なります。本研究では、申請者らのグループが開発した学習アナリティクス、クラウドソーシング、DTN(バケツリレー式のデータ転送方式)の技術を拡張・統合し、様々な学習空間において効率良く学習データを収集・転送し、有用フィードバックを提供できる分散協調型の学習アナリティクスプラットフォームの研究開発を行います。アフリカの教育機関と連携してユーザ中心の手法でデザイン・開発を行い、開発途上地域におけるエビデンスに基づく教育改善に貢献することを目指します。

    CiNii Research

Educational Activities

  • 2023 - Current KIKAN Education Seminar (English)
    2024 - Current (IUPE) Computer Programming Exercise
    2024 - Current Methodologies for practical data analysis
    2023 - Current KIKAN Education Seminar
    2023 - Current Programming Exercise (Python)

Class subject

  • プログラミング演習

    2023.12 - 2024.2   Winter quarter

  • プログラミング演習

    2023.6 - 2023.8   Summer quarter

  • 基幹教育セミナー

    2023.6 - 2023.8   Summer quarter

  • プログラミング演習

    2023.4 - 2023.9   First semester

  • プログラミング演習(P)

    2025.12 - 2026.2   Winter quarter

  • プログラミング演習(P)

    2025.10 - 2026.3   Second semester

  • 〔学際〕情報学H

    2025.6 - 2025.8   Summer quarter

  • 情報科学Ⅱ

    2025.6 - 2025.8   Summer quarter

  • 実データ解析技法2

    2025.6 - 2025.8   Summer quarter

  • 基幹教育セミナー

    2025.6 - 2025.8   Summer quarter

  • 〔学際〕情報学G

    2025.4 - 2025.6   Spring quarter

  • 情報科学Ⅰ

    2025.4 - 2025.6   Spring quarter

  • 実データ解析技法

    2025.4 - 2025.6   Spring quarter

  • プログラミング演習(P)

    2024.12 - 2025.2   Winter quarter

  • プログラミング演習(P)

    2024.12 - 2025.2   Winter quarter

  • 〔学際〕情報学G

    2024.10 - 2024.12   Fall quarter

  • 実データ解析技法

    2024.10 - 2024.12   Fall quarter

  • 基幹教育セミナー

    2024.6 - 2024.8   Summer quarter

  • Computer Programming Exercise

    2024.4 - 2024.9   First semester

  • 実データ解析技法

    2024.4 - 2024.6   Spring quarter

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FD Participation

  • 2023.4   Role:Participation   Title:令和5年度 第1回全学FD(新任教員の研修)The 1st All-University FD (training for new faculty members) in FY2023

    Organizer:University-wide