研究者業績

高見 享佑

タカミ キョウスケ  (Kyosuke Takami)

基本情報

所属
大阪教育大学 教育協働学科(理数情報教育系)/数理・知能 情報部門 准教授
学位
博士(理学)(2019年9月 大阪大学)

ORCID ID
 https://orcid.org/0000-0002-0913-4641
J-GLOBAL ID
202101001141418602
researchmap会員ID
R000019012

論文

 38
  • Kyosuke Takami, Ryosuke Nakamoto, Yiling Dai, Brendan Flanagan, Hiroaki Ogata
    Artificial Intelligence in Education, Practitioners Industry and Policy (PIP) track, AIED 2025 2025年7月  査読有り筆頭著者
  • Kyosuke Takami, Satoshi Sekine, Yusuke Miyao
    Proceedings of the 18th International Conference on Educational Data Mining 2025年7月  査読有り筆頭著者
  • Ryosuke Nakamoto, Brendan Flanagan, Yiling Dai, Kyosuke Takami, Hiroaki Ogata
    Interactive Learning Environments 2025年4月  査読有り
  • Yiling Dai, Heinz Ulrich Hoppe, Brendan Flanagan, Kyosuke Takami, Hiroaki Ogata
    Smart Learning Environments 11(61) 2024年12月  査読有り
  • Kyosuke Takami
    32nd International Conference on Computers in Education Conference Proceedings (ICCE 2024) 2024年11月  査読有り筆頭著者最終著者責任著者
  • Ola Erstad, Miroslava Černochová, Gerald Knezek, Takahisa Furuta, Kyosuke Takami, Changhao Liang
    Technology, Knowledge and Learning 2024年8月20日  査読有り
  • LLM-jp, :, Akiko Aizawa, Eiji Aramaki, Bowen Chen, Fei Cheng, Hiroyuki Deguchi, Rintaro Enomoto, Kazuki Fujii, Kensuke Fukumoto, Takuya Fukushima, Namgi Han, Yuto Harada, Chikara Hashimoto, Tatsuya Hiraoka, Shohei Hisada, Sosuke Hosokawa, Lu Jie, Keisuke Kamata, Teruhito Kanazawa, Hiroki Kanezashi, Hiroshi Kataoka, Satoru Katsumata, Daisuke Kawahara, Seiya Kawano, Atsushi Keyaki, Keisuke Kiryu, Hirokazu Kiyomaru, Takashi Kodama, Takahiro Kubo, Yohei Kuga, Ryoma Kumon, Shuhei Kurita, Sadao Kurohashi, Conglong Li, Taiki Maekawa, Hiroshi Matsuda, Yusuke Miyao, Kentaro Mizuki, Sakae Mizuki, Yugo Murawaki, Ryo Nakamura, Taishi Nakamura, Kouta Nakayama, Tomoka Nakazato, Takuro Niitsuma, Jiro Nishitoba, Yusuke Oda, Hayato Ogawa, Takumi Okamoto, Naoaki Okazaki, Yohei Oseki, Shintaro Ozaki, Koki Ryu, Rafal Rzepka, Keisuke Sakaguchi, Shota Sasaki, Satoshi Sekine, Kohei Suda, Saku Sugawara, Issa Sugiura, Hiroaki Sugiyama, Hisami Suzuki, Jun Suzuki, Toyotaro Suzumura, Kensuke Tachibana, Yu Takagi, Kyosuke Takami, Koichi Takeda, Masashi Takeshita, Masahiro Tanaka, Kenjiro Taura, Arseny Tolmachev, Nobuhiro Ueda, Zhen Wan, Shuntaro Yada, Sakiko Yahata, Yuya Yamamoto, Yusuke Yamauchi, Hitomi Yanaka, Rio Yokota, Koichiro Yoshino
    2024年7月4日  
    This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and strong Japanese LLMs, and as of this writing, more than 1,500 participants from academia and industry are working together for this purpose. This paper presents the background of the establishment of LLM-jp, summaries of its activities, and technical reports on the LLMs developed by LLM-jp. For the latest activities, visit https://llm-jp.nii.ac.jp/en/.
  • Hiroaki Ogata, Changhao Liang, Yuko Toyokawa, Chia-Yu Hsu, Kohei Nakamura, Taisei Yamauchi, Brendan Flanagan, Yiling Dai, Kyosuke Takami, Izumi Horikoshi, Rwitajit Majumdar
    Technology, Knowledge and Learning 2024年7月3日  査読有り
    Abstract This paper explores co-design in Japanese education for deploying data-driven educational technology and practice. Although there is a growing emphasis on data to inform educational decision-making and personalize learning experiences, challenges such as data interoperability and inconsistency with teaching goals prevent practitioners from participating. Co-design, characterized by involving various stakeholders, is instrumental in addressing the evolving needs of technology deployment. Japan's educational context aligns with co-design implementation, with a learning and evidence analytics infrastructure facilitating data collection and analysis. From the Japanese co-design practice of educational technologies, the paper highlights a 6-phase co-design framework: motivate, pilot, implement, refine, evaluate, and maintain. The practices focus on data-driven learning strategies, technology interventions, and across-context dashboards, covering assorted learning contexts in Japan. By advocating for a co-design culture and data-driven approaches to enhance education in Japan, we offer insights for education practitioners, policymakers, researchers, and industry developers.
  • Takahisa Furuta, Ola Erstad, Gerald Knezek, Anantha Duraia, Kyosuke Takami, Changhao Liang
    EdMedia+ Innovate Learning 2024年7月  査読有り
  • Ryosuke Nakamoto, Brendan Flanagan, Yiling Dai, Kyosuke Takami, Hiroaki Ogata
    The 24th International Conference on Advanced Learning Technologies (ICALT 2024) 2024年7月  査読有り
  • Ryosuke Nakamoto, Brendan Flanagan, Yiling Dai, Kyosuke Takami, Hiroaki Ogata
    The 24th International Conference on Advanced Learning Technologies (ICALT 2024) 2024年7月  査読有り
  • Kyosuke Takami, Brendan Flanagan, Yiling Dai, Hiroaki Ogata
    International Journal of Distance Education Technologies 22(1) 1-23 2024年2月7日  査読有り筆頭著者
    <p>Explainable recommendation, which provides an explanation about why a quiz is recommended, helps to improve transparency, persuasiveness, and trustworthiness. However, little research examined the effectiveness of the explainable recommender, especially on academic performance. To survey its effectiveness, the authors evaluate the math academic performance among middle school students (n=115) by giving pre- and post-test questions based evaluation techniques. During the pre- and post-test periods, students were encouraged to use the Bayesian Knowledge Tracing model based explainable recommendation system. To evaluate how well the students were able to do what they could not do, the authors defined growth rate and found recommended quiz clicked counts had a positive effect on the total number of solved quizzes (R=0.343, P=0.005) and growth rate (R=0.297, P=0.017) despite no correlation between the total number of solved quizzes and growth rate. The results suggest that the use of an explainable recommendation system that learns efficiently will enable students to do what they could not do before.</p>
  • Ryosuke Nakamoto, Brendan Flanagan, Yiling Dai, Taisei Yamauchi, Kyosuke Takami, Hiroaki Ogata
    Res. Pract. Technol. Enhanc. Learn. 20 19-19 2024年  
  • Kyosuke Takami, Brendan Flanagan, Yiling Dai, Hiroaki Ogata
    Smart Learning Environments 10(65) 2023年12月  査読有り筆頭著者
  • Kyosuke Takami
    The workshop “The Applications of Generative Artificial Intelligence (GAI) in Education” of the 31st International Conference on Computers in Education (ICCE 2023) 2023年12月  査読有り筆頭著者最終著者責任著者
  • Taisei Yamauchi, Ryosuke Nakamoto, Yiling Dai, Kyosuke Takami, Brendan Flanagan, Hiroaki Ogata
    31th International Conference on Computers in Education Conference Proceedings 2023 2023年12月  査読有り
  • Taisei Yamauchi, Yuta Nakamizo, Kyosuke Takami, Rwitajit Majumdar, Hiroaki Ogata
    31th International Conference on Computers in Education Conference Proceedings 2023 2023年12月  査読有り
  • Ryosuke Nakamoto, Brendan Flanagan, Yiling Dai, Taisei Yamauchi, Kyosuke Takami, Hiroaki Ogata
    Sustainability 15(21) 15577-15577 2023年11月2日  
    This research introduces the self-explanation-based automated feedback (SEAF) system, aimed at alleviating the teaching burden through real-time, automated feedback while aligning with SDG 4’s sustainability goals for quality education. The system specifically targets the enhancement of self-explanation, a proven but challenging cognitive strategy that bolsters both conceptual and procedural knowledge. Utilizing a triad of core feedback mechanisms—customized messages, quality assessments, and peer-generated exemplars—SEAF aims to fill the gap left by traditional and computer-aided self-explanation methods, which often require extensive preparation and may not provide effective scaffolding for all students. In a pilot study involving 50 junior high students, those with initially limited self-explanation skills showed significant improvement after using SEAF, achieving a moderate learning effect. A resounding 91.7% of participants acknowledged the system’s positive impact on their learning. SEAF’s automated capabilities serve dual purposes: they offer a more personalized and scalable approach to student learning while simultaneously reducing the educators’ workload related to feedback provision.
  • Ryosuke Nakamoto, Brendan Flanagan, Taisei Yamauchi, Yiling Dai, Kyosuke Takami, Hiroaki Ogata
    Computers 12(11) 217-217 2023年10月24日  査読有り
    In the realm of mathematics education, self-explanation stands as a crucial learning mechanism, allowing learners to articulate their comprehension of intricate mathematical concepts and strategies. As digital learning platforms grow in prominence, there are mounting opportunities to collect and utilize mathematical self-explanations. However, these opportunities are met with challenges in automated evaluation. Automatic scoring of mathematical self-explanations is crucial for preprocessing tasks, including the categorization of learner responses, identification of common misconceptions, and the creation of tailored feedback and model solutions. Nevertheless, this task is hindered by the dearth of ample sample sets. Our research introduces a semi-supervised technique using the large language model (LLM), specifically its Japanese variant, to enrich datasets for the automated scoring of mathematical self-explanations. We rigorously evaluated the quality of self-explanations across five datasets, ranging from human-evaluated originals to ones devoid of original content. Our results show that combining LLM-based explanations with mathematical material significantly improves the model’s accuracy. Interestingly, there is an optimal limit to how many synthetic self-explanation data can benefit the system. Exceeding this limit does not further improve outcomes. This study thus highlights the need for careful consideration when integrating synthetic data into solutions, especially within the mathematics discipline.
  • Taisei Yamauchi, Brendan Flanagan, Ryosuke Nakamoto, Yiling Dai, Kyosuke Takami, Hiroaki Ogata
    Smart Learning Environments 10(1) 2023年10月18日  査読有り
    Abstract In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and difficult to scale. It recognizes that automated labeling eases the workload on experts, as seen in previous studies using automatic classification algorithms for research papers and Japanese mathematical exercises. However, these studies didn’t delve into fine-grained labeling. In addition to that, as the use of materials in the system becomes more widespread, paper materials are transformed into PDF formats, which can lead to incomplete extraction. However, there is less emphasis on labeling incomplete mathematical sentences to tackle this problem in the previous research. This study aims to achieve precise automated classification even from incomplete text inputs. To tackle these challenges, we propose a mathematical exercise labeling algorithm that can handle detailed labels, even for incomplete sentences, using word n-grams, compared to the state-of-the-art word embedding method. The results of the experiment show that mono-gram features with Random Forest models achieved the best performance with a macro F-measure of 92.50%, 61.28% for 24-class labeling and 297-class labeling tasks, respectively. The contribution of this research is showing that the proposed method based on traditional simple n-grams has the ability to find context-independent similarities in incomplete sentences and outperforms state-of-the-art word embedding methods in specific tasks like classifying short and incomplete texts.
  • Yiling Dai, Kyosuke Takami, Brendan Flanagan, Hiroaki Ogata
    Research and Practice in Technology Enhanced Learning 19 020-020 2023年9月12日  査読有り
    Recommender systems can provide personalized advice on learning for individual students. Providing explanations of those recommendations are expected to increase the transparency and persuasiveness of the system, thus improve students’ adoption of the recommendation. Little research has explored the explanations’ practical effects on learning performance except for the acceptance of recommended learning activities. The recommendation explanations can improve the learning performance if the explanations are designed to contribute to relevant learning skills. This study conducted a comparative experiment (N = 276) in high school classrooms, aiming to investigate whether the use of an explainable math recommender system improves students’ learning performance. We found that the presence of the explanations had positive effects on students’ learning improvement and perceptions of the systems, but not the number of solved quizzes during the learning task. These results imply the possibility that the recommendation explanations may affect students’ meta-cognitive skills and their perceptions, which further contribute to students’ learning improvement. When separating the students based on their prior math abilities, we found a significant correlation between the number of viewed recommendations and the final learning improvement for the students with lower math abilities. This indicates that the students with lower math abilities may benefit from reading their learning progress indicated in the explanations. For students with higher math abilities, their learning improvement was more related to the behavior to select and solve recommended quizzes, which indicates a necessity of more sophisticated and interactive recommender system.
  • Hiroaki Ogata, Brendan Flanagan, Kyosuke Takami, Yiling Dai, Ryosuke Nakamoto, Kensuke Takii
    Research and Practice in Technology Enhanced Learning 19 019-019 2023年8月28日  査読有り
    As artificial intelligence systems increasingly make high-stakes recommendations and decisions automatically in many facets of our lives, the use of explainable artificial intelligence to inform stakeholders about the reasons behind such systems has been gaining much attention in a wide range of fields, including education. Also, in the field of education there has been a long history of research into self-explanation, where students explain the process of their answers. This has been recognized as a beneficial intervention to promote metacognitive skills, however, there is also unexplored potential to gain insight into the problems that learners experience due to inadequate prerequisite knowledge and skills that are required, or in the process of their application to the task at hand. While this aspect of self-explanation has been of interest to teachers, there is little research into the use of such information to inform educational AI systems. In this paper, we propose a system in which both students and the AI system explain to each other their reasons behind decisions that were made, such as: self-explanation of student cognition during the answering process, and explanation of recommendations based on internal mechanizes and other abstract representations of model algorithms.
  • Ryosuke Nakamoto, Brendan Flanagan, Yiling Dai, Kyosuke Takami, Hiroaki Ogata
    Research and Practice in Technology Enhanced Learning 19 016-016 2023年8月16日  査読有り
    Self-explanation is a widely recognized and effective pedagogical method. Previous research has indicated that self-explanation can be used to evaluate students’ comprehension and identify their areas of difficulty on mathematical quizzes. However, most analytical techniques necessitate pre-labeled materials, which limits the potential for large-scale study. Conversely, utilizing collected self-explanations without supervision is challenging because there is little research on this topic. Therefore, this study aims to investigate the feasibility of automatically generating a standardized self-explanation sample answer from unsupervised collected self-explanations. The proposed model involves preprocessing and three machine learning steps: vectorization, clustering, and extraction. Experiments involving 1,434 self-explanation answers from 25 quizzes indicate that 72% of the quizzes generate sample answers containing all the necessary knowledge components. The similarity between human-generated and machine-generated sentences was significant with moderate positive correlation, r(23) = .48, p &lt; .05.The best-performing generative model also achieved a high BERTScore of 0.715. Regarding the readability of the generated sample answers, the average score of the human-generated sentences was superior to that of the machine-generated ones. These results suggest that the proposed model can generate sample answers that contain critical knowledge components and can be further improved with BERTScore. This study is expected to have numerous applications, including identifying students’ areas of difficulty, scoring self-explanations, presenting students with reference materials for learning, and automatically generating scaffolding templates to train self-explanation skills.
  • Rwitajit Majumdar, Kyosuke Takami, Hiroaki Ogata
    The 23rd International Conference on Advanced Learning Technologies (ICALT 2023) 2023年7月  査読有り
  • Kyosuke Takami, Brendan Flanagan, Yiling Dai, Hiroaki Ogata
    Companion Proceedings 13th International Conference on Learning Analytics & Knowledge (LAK23) 2023年3月  査読有り筆頭著者
  • Yiling Dai, Brendan Flanagan, Kyosuke Takami, Hiroaki, Ogata
    Companion Proceedings 13th International Conference on Learning Analytics & Knowledge (LAK23) 2023年3月  査読有り
  • Kyosuke TAKAMI, Gou MIYABEb, Brendan FLANAGANa, Hiroaki OGATA
    30th International Conference on Computers in Education Conference Proceedings 2022年11月  査読有り筆頭著者
  • Taisei YAMAUCHI, Kyosuke TAKAMI, Brendan FLANAGAN, Hiroaki OGATA
    30th International Conference on Computers in Education Conference Proceedings 2022年11月  査読有り
  • Yiling DAI, Kyosuke TAKAMI, Brendan FLANAGAN, Hiroaki OGATA
    30th International Conference on Computers in Education Conference Proceedings 2022年11月  査読有り
  • Nakamoto, R, Flanagan, B, Dai, Y, Takami, K, Ogata, H
    International Conference on Artificial Intelligence in Education (AIED 2022) 2022年7月  査読有り
  • Kyosuke Takami, Yiling Dai, Brendan Flanagan, Hiroaki Ogata
    LAK22: 12th International Learning Analytics and Knowledge Conference 458-464 2022年3月21日  査読有り筆頭著者
  • Takami, K, Flanagan, B, Majumdar, R, Ogata, H
    Companion Proceedings of the 12th International Conference on Learning Analytics and Knowledge. 2022年3月  査読有り筆頭著者
  • Dai, Y, Flanagan, B, Takami, K, Ogata, H
    Companion Proceedings of the 12th International Conference on Learning Analytics and Knowledge. 2022年3月  査読有り
  • Takami, K., Flanagan, B., Dai, Y., Ogata, H.
    29th International Conference on Computers in Education Conference Proceedings. 2021年11月  査読有り筆頭著者
  • Nakamoto R., Flanagan B., Takami K., Dai Y., Ogata H.
    29th International Conference on Computers in Education Conference Proceedings. 2021年11月  査読有り
  • Brendan Flanagan, Kyosuke Takami, Kensuke Takii, Yiling Dai, Rwitajit Majumdar, Hiroaki Ogata
    29TH INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION (ICCE 2021), VOL I 404-409 2021年  査読有り
  • Kyosuke Takami, Masahiko Haruno
    eNeuro 7(3) 1-13 2020年5月  査読有り筆頭著者
  • Kyosuke Takami, Masahiko Haruno
    Social cognitive and affective neuroscience 14(1) 23-33 2019年1月4日  査読有り筆頭著者
    Recent studies have shown that the reactions of bystanders who witness bullying significantly affect whether the bullying persists. However, the underlying behavioral and neural mechanisms that determine a peer-influenced bystander's participation in bullying remain largely unknown. Here, we designed a new 'catch-ball' task where four players choose to throw a sequence of normal or strong (aggressive) balls in turn and examined whether the players (n = 43) participated in other players' bullying. We analyzed behaviors with a computational model that quantifies the tendencies of a participant's (i) baseline propensity for bullying, (ii) reactive revenge, (iii) conformity to bullying, and (iv) capitulation to threat and estimated these effects on the choice of balls. We found only conformity had a positive effect on the throwing of strong balls. Furthermore, we identified a correlation between a participant's conformity and social anxiety. Our mediation analysis of resting-state functional magnetic resonance imaging revealed that there were significant relationships of each participant's functional connectivity between the amygdala and right temporoparietal junction (TPJ) and social anxiety to the participant's conformity to bullying. We also found that amygdala-TPJ connectivity partially mediated the relationship between social anxiety and conformity. These results highlighted the anxiety-based conformity and amygdala network on peer-influenced bystander participation in bullying.

MISC

 7

書籍等出版物

 3

講演・口頭発表等

 13

共同研究・競争的資金等の研究課題

 3

社会貢献活動

 9

メディア報道

 3