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所属 |
大学院理工学研究科 数理・電気電子情報学専攻 人間情報工学コース |
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生年 |
1986年 |
出身大学院 【 表示 ／ 非表示 】
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2011年10月-2014年09月
東京大学 新領域創成科学研究科 社会文化環境学 博士課程 修了
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2008年09月-2011年07月
北京大学 地球と空間科学学院 地図学と地理情報システム 修士課程 修了
学会（学術団体）・委員会 【 表示 ／ 非表示 】
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2020年09月-継続中
アメリカ合衆国
計算機協会(ACM)
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2020年-2021年
日本国
Society for Learning Analytics Research (SoLAR)
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2018年12月-継続中
日本国
情報処理学会
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2012年08月-継続中
日本国
日本地図学会
研究分野 【 表示 ／ 非表示 】
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自然科学一般 / 地球人間圏科学
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人文・社会 / 地理学
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情報通信 / 学習支援システム
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情報通信 / ヒューマンインタフェース、インタラクション
学位論文 【 表示 ／ 非表示 】
研究等業績 【 表示 ／ 非表示 】
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Cross-language font style transfer
Chenhao Li, Yuta Taniguchi, Min Lu, Shin’ichi Konomi, Hajime Nagahara
Applied Intelligence ( Springer Science and Business Media LLC ) 2023年02月 [査読有り]
研究論文（学術雑誌）
Abstract
In this paper, we propose a cross-language font style transfer system that can synthesize a new font by observing only a few samples from another language. Automatic font synthesis is a challenging task and has attracted much research interest. Most previous works addressed this problem by transferring the style of the given subset to the content of unseen ones. Nevertheless, they only focused on the font style transfer in the same language. In many cases, we need to learn font style from one language and then apply it to other languages. Existing methods make this difficult to accomplish because of the abstraction of style and language differences. To address this problem, we specifically designed the network into a multi-level attention form to capture both local and global features of the font style. To validate the generative ability of our model, we constructed an experimental font dataset of 847 fonts, each containing English and Chinese characters with the same style. Results show that our model generates 80.3% of users’ preferred images compared with state-of-the-art models. -
インドアARナビゲーション実現のための基本フレームワークの提案と実証実験
有川 正俊, 大場 康平, 伊東 慎平, 佐藤 諒, ルウ ミン
情報処理学会論文誌 63 ( 12 ) 1821 - 1829 2022年12月 [査読有り]
研究論文（学術雑誌） 国内共著
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Exploring jump back behavior patterns and reasons in e-book system
Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi
Smart Learning Environments ( Springer Science and Business Media LLC ) 9 ( 2 ) 1 - 23 2022年01月 [査読有り]
研究論文（学術雑誌） 国内共著
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.
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CourseQ: the impact of visual and interactive course recommendation in university environments
Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi
Research and Practice in Technology Enhanced Learning ( Springer ) 16 ( 1 ) 1 - 24 2021年12月 [査読有り]
研究論文（学術雑誌） 国内共著
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.
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Investigating course choice motivations in university environments
Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi
Smart Learning Environments ( Springer Science and Business Media LLC ) 8 ( 31 ) 1 - 18 2021年11月 [査読有り]
研究論文（学術雑誌） 国内共著
<title>Abstract</title>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.
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Adaptive visualization of tourists' preferred spots and streets using trajectory articulation
Iori Sasaki, Masatoshi Arikawa, Min Lu
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility ( ACM ) 27 - 32 2022年11月 [査読有り]
研究論文（国際会議プロシーディングス） 国内共著
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Understanding Student Slide Reading Patterns During the Pandemic
Boxuan Ma, Min Lu, Shin’ichi Konomi
Proceedings of 18th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2021) ( IADIS Press ) 87 - 94 2021年10月 [査読有り]
研究論文（国際会議プロシーディングス） 国内共著
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Exploration and Explanation: An Interactive Course Recommendation System for University Environments
Boxuan Ma, Min Lu, Yuta Taniguchi, Shin'ichi Konomi
CEUR Workshop Proceedings ( CEUR-WS ) 2903 2021年04月 [査読有り]
研究論文（国際会議プロシーディングス） 国内共著
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.
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Few-Shot Font Style Transfer Between Different Languages
Chenhao Li, Yuta Taniguchi, Min Lu, Shin'ichi Konomi
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021 ( Institute of Electrical and Electronics Engineers Inc. ) 433 - 442 2021年01月 [査読有り]
研究論文（国際会議プロシーディングス） 国内共著
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Factors of the use of learning analytics dashboard that affect metacognition
Li Chen, Min Lu, Yoshiko Goda, Atsushi Shimada, Masanori Yamada
17th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2020 ( IADIS Press ) 295 - 302 2020年11月 [査読有り]
研究論文（国際会議プロシーディングス） 国内共著
© 2020 17th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2020. All rights reserved. In this study, we used a learning analytics dashboard (LAD) in a higher education course to support students' metacognition and evaluated the effects of its use. The LAD displays students' reading path and specific behaviors when viewing digital learning materials. The study was conducted on 53 university students to identify the factors that affected metacognition changes in terms of their awareness and behavior dimensions when using the LAD. In terms of results, first, the students' perception of visual attraction for the LAD, and behaviors related to reflection such as deleting annotations they had previously added, positively affected the changes in the knowledge of cognition dimension of metacognition. Second, students' perception of behavioral changes by using the LAD had positive effects on the regulation of cognition dimension of metacognition. However, the behaviors of using some cognitive tools, negatively affected knowledge of cognition, which indicated the necessity to provide more guidance or feedback to students.
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Learning Analytics Dashboard Supporting Metacognition
Li Chen, Min Lu, Yoshiko Goda, Atsushi Shimada, Masanori Yamada
Balancing the Tension between Digital Technologies and Learning Sciences ( Springer International Publishing ) 129 - 149 2021年02月 [査読有り]
国内共著
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Visualizing Studying Activities for a Learning Dashboard Supporting Meta-cognition for Students
Min Lu, Li Chen, Yoshiko Goda, Atsushi Shimada, Masanori Yamada
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ( Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ) 12203 LNCS 569 - 580 2020年07月 [招待有り]
国内共著
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Development of a Learning Dashboard Prototype Supporting Meta-cognition for Students
Min Lu, Li Chen, Yoshiko Goda, Atsushi Shimada, Masanori Yamada
Companion Proceedings 10th International Conference on Learning Analytics & Knowledge (LAK20) 104 - 106 2020年03月
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Exploring the Design Space for Explainable Course Recommendation Systems in University Environments
Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi
Companion Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK20) 492 - 499 2020年03月
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Understanding Jump Back Behaviors in E-book System
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) 623 - 631 2020年03月
◆原著論文【 表示 ／ 非表示 】
◆国際会議プロシーディングス【 表示 ／ 非表示 】
◆その他【 表示 ／ 非表示 】
Book（書籍） 【 表示 ／ 非表示 】
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Ubiquitous Mapping: Perspectives from Japan - Chapter 2: Ubiquitous Digital Storytelling with Local and Dynamic Georeferencing of Analog Maps
Masatoshi Arikawa, Min Lu ( 担当： 共著 )
Springer Singapore 2022年07月 ISBN: 978-981-19-1535-2
学術書
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あいまいな時空間情報の分析 「第10章 イラストマップをGPS連動させるストーリーマッピング作成利用環境」
有川正俊, Lu Min, Si Ruochen ( 担当： 共著 )
古今書院 2020年 ISBN: 9784772220293
教科書・概説・概論
科研費（文科省・学振）獲得実績 【 表示 ／ 非表示 】
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学習行動改善モデルに基づくラーニングアナリティクス基盤の開発と評価
基盤研究(A)
研究期間: 2022年04月 - 2026年03月 代表者: 山田 政寛
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教育学領域と情報学の融合による教育データリテラシー習得モデル構築への挑戦
挑戦的研究（開拓・萌芽）
研究期間: 2021年07月 - 2025年03月 代表者: 山田 政寛
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Research and Development of a Novel Learning Support Environment on Synchronized Multiple Devices with Affordance
若手研究
研究期間: 2020年04月 - 2023年03月 代表者: Lu Min
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連鎖性を考慮した地震による土砂災害予測手法の高度化と実用化研究
国際共同研究加速基金
研究期間: 2019年10月 - 2023年03月 代表者: 陳 光斉
共同研究実施実績 【 表示 ／ 非表示 】
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教育大航海時代の羅針盤：学習分析の信頼基盤ReLAXの創出
提供機関: 九州大学 その他 国内共同研究
研究期間： 2022年10月 - 2028年03月 代表者: 島田 敬士
学会等発表 【 表示 ／ 非表示 】
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多様な分析粒度を実現するラーニングアナリティクス基盤の開発
山田 政寛, Lu Min, 谷口 雄太, 大久保 文哉, 陳 莉, 谷口 倫一郎
日本教育工学会2022年春季全国大会 (鳴門教育大学（オンライン）) 2022年03月 - 2022年03月 日本教育工学会
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Smart code recommendation system for supporting the learning process of programming beginners
Likun Liu, Yuta Taniguchi, Min Lu, Shin’ichi Konomi
電子情報通信学会教育工学研究会 2020年12月 - 2020年12月
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Multi-level Attention Networks for Font Style Transfer between Different Languages
Chenhao Li, Yuta Taniguchi, Min Lu, Shin’ichi Konomi
Visual Computing 2020 2020年12月 - 2020年12月
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学習者のメタ認知喚起を支援するダッシュボード"メタボード"の形成的評価
山田政寛, Min Lu, 陳莉, 合田美子, 島田敬士
教育システム情報学会第45回全国大会 (オンライン) 2020年09月 - 2020年09月 一般社団法人 教育システム情報学会
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Development of a Learning Dashboard Prototype Supporting Meta-cognition for Students
Min Lu, Li Chen, Yoshiko Goda, Atsushi Shimada, Masanori Yamada
10th International Conference on Learning Analytics & Knowledge (LAK20) (Frankfurt, Germany (Online)) 2020年03月 - 2020年03月 Society for Learning Analytics Research
担当授業科目（学内） 【 表示 ／ 非表示 】
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2022年10月-2022年12月
基礎情報学C
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2022年10月-2022年11月
基礎情報学A
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2022年10月-2022年11月
基礎情報学B