研究等業績 - 国際会議プロシーディングス - LU MIN
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Generative Live Commentaries Interacting with Geospatial Context for Promoting Local Festivals
Iori Sasaki, Masatoshi Arikawa, Min Lu, Tomihiro Utsumi, Ryo Sato
Proceedings of the 2024 8th International Conference on Big Data and Internet of Things ( ACM ) 125 - 131 2024年09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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Space-Division-Based Pseudo-Occlusion in 3D Trajectory Data Visualization for Indoor AR Navigation
Yuhan Jin, Xiangling Peng, Kohei Oba, Masatoshi Arikawa, Min Lu, Tomihiro Utsumi, Ryo Sato
Proceedings of the 2024 8th International Conference on Big Data and Internet of Things ( ACM ) 138 - 143 2024年09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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Iori Sasaki, Masatoshi Arikawa, Min Lu, Tomihiro Utsumi, Ryo Sato
26th International Conference on Mobile Human-Computer Interaction ( ACM ) ( 24 ) 1 - 5 2024年09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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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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Kaori Tamura, Min Lu, Shin’ichi Konomi, Kohei Hatano, Miyuki Inaba, Misato Oi, Tsuyoshi Okamoto, Fumiya Okubo, Atsushi Shimada, Jingyun Wang, Masanori Yamada, Yuki Yamada
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ( Springer, Cham ) 11577 LNCS 469 - 481 2019年06月 [招待有り]
研究論文（国際会議プロシーディングス）
© 2019, Springer Nature Switzerland AG. Extended learning environments involving system to collect data for learning analytics and to support learners will be useful for all-age education. As the first steps towards to build new learning environments, we developed a system for multimodal learning analytics using eye-tracker and EEG measurement, and inclusive user interface design for elderly learners by dual-tablet system. Multimodal learning analytics system can be supportive to extract where and how learners with varied backgrounds feel difficulty in learning process. The eye-tracker can retrieve information where the learners paid attention. EEG signals will provide clues to estimate their mental states during gazes in learning. We developed simultaneous measurement system of these multimodal responses and are trying to integrate the information to explore learning problems. A dual-tablet user interface with simplified visual layers and more intuitive operations was designed aiming to reduce the physical and mental loads of elderly learners. A prototype was developed based on a cross-platform framework, which is being refined by iterative formative evaluations participated by elderlies, in order to improve the usability of the interface design. We propose a system architecture applying the multimodal learning analytics and the user-friendly design for elderly learners, which couples learning analytics “in the wild” environment and learning analytics in controlled lab environments.