九州大学 研究者情報
論文一覧
大久保 文哉(おおくぼ ふみや) データ更新日:2022.06.28

准教授 /  システム情報科学研究院 情報知能工学部門


原著論文
1. Erwin D. Lopez Z, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada, Exploring the use of probabilistic latent representations to encode the students' reading characteristics, Proceedings of the 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior, 1-10, 2022.03.
2. Brendan Flanagan, Atsushi Shimada, Fumiya Okubo, Huiyong Li, Rwitajit Majumdar, Hiroaki Ogata, The 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior, Companion Proceedings 12th International Conference on Learning Analytics & Knowledge (LAK22), 152-155, 2022.03.
3. Sukrit Leelaluk, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada, Predicting student performance based on Lecture Materials data using Neural Network Models, Proceedings of the 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior, 1-10, 2022.03.
4. Ryusuke Murata, Fumiya Okubo, Tsubasa Minematsu, Yuta Taniguchi, Atsushi Shimada, New Perspective on Input Feature Analysis for Early Feedback by Student Performance Prediction Considering the Future Effect, Companion Proceedings 12th International Conference on Learning Analytics & Knowledge (LAK22), 95-97, 2022.03.
5. Yuta Taniguchi, Takuro Owatari, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada, Live Sharing of Learning Activities on E-Books for Enhanced Learning in Online Classes, Sustainability, 10.3390/su14126946, 14, 12, 6946-6946, 2022.06, While positive effects of imitating other learners have been reported, the recent increases in the number of online classes have seriously limited opportunities to learn how others are learning. Providing information about others’ learning activities through dashboards could be a solution, but few studies have targeted learning activities on e-textbook systems; it remains unclear what information representations would be useful and how they would affect learning. We developed a dashboard system that enables live sharing of students’ learning activities on e-textbooks. An experiment was conducted applying the dashboard in an online class to evaluate its impact. The results of questionnaires and quizzes were analyzed along with learning activities on the e-textbook system. From the questionnaire results, the most useful feedback types were identified. Regarding the impact on learning, the study found that a higher percentage of students who used the dashboard followed the progress of the class than those who did not. The study also found that students who used the dashboard were more likely to achieve higher quiz scores than those who did not. This study is the first to reveal what specific feedback is useful and to successfully investigate the impact of its use on learning..
6. Boyi Li, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada, How Does Analysis of Handwritten Notes Provide Better Insights for Learning Behavior?, LAK22: 12th International Learning Analytics and Knowledge Conference, 10.1145/3506860.3506915, 2022.03.
7. Yuta Taniguchi, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada, Coding Trajectory Map: Student Programming Situations Made Visually Locatable, Companion Proceedings 12th International Conference on Learning Analytics & Knowledge (LAK22), 98-100, 2022.03.
8. Fumiya Okubo, Kaoru Fujioka, Takashi Yokomori, Chemical Reaction Regular Grammars, New Generation Computing, 10.1007/s00354-022-00160-8, 2022.03.
9. Kaoru Fujioka, Fumiya Okubo, Takashi Yokomori, $$mathcal {L}$$-reduction computation revisited, Acta Informatica, 10.1007/s00236-022-00418-0, 2022.03.
10. Takashi Yokomori, Fumiya Okubo, Theory of reaction automata: a survey, Journal of Membrane Computing, 10.1007/s41965-021-00070-6, 2021.03.
11. Fumiya Okubo, Takashi Yokomori, On the computing powers of L-reductions of insertion languages, Theoretical Computer Science, 10.1016/j.tcs.2020.11.029, 2021.03, © 2020 We investigate the computing power of the following language operation %: Given two languages L1 over Σ and L2 over Γ with Γ⊂Σ, we consider the language operation L1%L2={u0u1⋯un|∃u=u0v1u1⋯vnun∈L1 and ∃vi∈L2(1≤∀i≤n)}. In this case we say that L(=L1%L2) is the L2-reduction of L1. This is extended to the language families as follows: L1%L2={L1%L2|L1∈L1,L2∈L2}. Among many works concerning Dyck-reductions, for the family of recursively enumerable languages RE, it was shown that LIN%{EQ}=RE (Jantzen & Petersen, 1994) with EQ={xnx‾n|n∈N} and that min-LIN%{D2}=RE (Hirose & Okawa, 1996, and Latteux & Turakainen, 1990), where LIN and min-LIN are the families of linear and minimal linear context-free languages, respectively. In this paper, we show that each recursively enumerable language L can be represented in the form L=K%D, for some K∈INS30 and a Dyck language D, where INS⁎0 (INS30) denotes the family of insertion languages (insertion languages where the maximum length of the string to be inserted is 3). We can refine it as INS⁎0%{D2}=RE, where D2 denotes the Dyck language over binary alphabet. For context-free languages, we show that INS30%F=CF, where F is the family of finite sets. This also derives that INS⁎0%{MIR}=CF with MIR={xx‾R|x∈{0,1}⁎}. Further, for regular languages, it is shown that each regular language R can be represented in the form R=K%F, for some K∈INS20 and a finite set F={abb‾a‾|a∈V}. We also present some results which characterize the computability and properties of L in the framework of L2-reduction of L1. It is intriguing to note that, from the DNA computing point of view, the notion of L-reduction is naturally motivated by a molecular biological functioning well-known as DNA(RNA) splicing occurring in most eukaryotic genes..
12. Li Chen, Koichi Inoue, Yoshiko Goda, Fumiya Okubo, Yuta Taniguchi, Misato Oi, Shin’ichi Konomi, Hiroaki Ogata, Masanori Yamada, Exploring Factors that Influence Collaborative Problem Solving Awareness in Science Education, Technology, Knowledge and Learning, 10.1007/s10758-020-09436-8, 25, 2, 337-366, 2020.06, © 2020, Springer Nature B.V. This study designed a science course following collaborative problem solving (CPS) processes, and examined the effect on students’ CPS awareness. The Limnic Eruption CPS course was implemented using a Moodle system in a tenth-grade class. Considering the complex and coordinated nature of CPS, in order to improve CPS skills, it is important to identify what are related with the development of all sub-skills of CPS. Thus this study aimed to determine potential factors that affect the use of CPS skills in students’ motivational and behavioral dimensions. Multiple data sources including learning tests, questionnaire feedback, and learning logs were collected and examined by learning analytics approach. The relationships between students’ CPS awareness with their learning motivation and learning behaviors were explored. The research findings indicated a significant positive correlation between CPS awareness and certain learning motivation factors and learning behavior factors. Considering the students’ individual differences in learning abilities, we also compared the results of high and low performance groups. As a result, low performers’ learning motivation and learning behaviors were correlated with the social domain of CPS awareness, while those of high performers were correlated with their cognitive awareness..
13. Li Chen, Nobuyuki Yoshimatsu, Yoshiko Goda, Fumiya Okubo, Yuta Taniguchi, Misato Oi, Shin’ichi Konomi, Atsushi Shimada, Hiroaki Ogata, Masanori Yamada, Direction of collaborative problem solving-based STEM learning by learning analytics approach, Research and Practice in Technology Enhanced Learning, 10.1186/s41039-019-0119-y, 14, 1, 2019.12, © 2019, The Author(s). The purpose of this study was to explore the factors that might affect learning performance and collaborative problem solving (CPS) awareness in science, technology, engineering, and mathematics (STEM) education. We collected and analyzed data on important factors in STEM education, including learning strategy and learning behaviors, and examined their interrelationships with learning performance and CPS awareness, respectively. Multiple data sources, including learning tests, questionnaire feedback, and learning logs, were collected and examined following a learning analytics approach. Significant positive correlations were found for the learning behavior of using markers with learning performance and CPS awareness in group discussion, while significant negative correlations were found for some factors of STEM learning strategy and learning behaviors in pre-learning with some factors of CPS awareness. The results imply the importance of an efficient approach to using learning strategies and functional tools in STEM education..
14. Jingyun Wang, Atsushi Shimada, Fumiya Okubo, E-book learner behaviors difference under two meaningful learning support environments, ICCE 2019 - 27th International Conference on Computers in Education, Proceedings, 1, 342-347, 2019.11, © 2019 International Conference on Computers in Education, Proceedings.All right reserved. In this paper, we present an ontology-based visualization support system for e-book learners, which provides not only a meaningful receptive learning environment but also a meaningful discovery learning environment. Those two environments are developed to help e-book learners to effectively construct their knowledge frameworks. A series of experiments were conducted on four undergraduate classes instructed by two professors (A and B): two classes(one guided by A and the other guided by B) were assigned as control groups and studied with one e-book chapter in receptive learning environment while another two classes (one guided by A and the other guided by B) were assigned as experimental groups and studied with the same e-book chapter in discovery learning environment. For analyzing the learner behavior, K-means clustering algorithm is performed not only by considering the number of total command actions and the cumulative duration of stay on target pages as learner features, but also by considering the duration of stay on each target page (in total 15 pages) as learner features. Learners’ behavior differences in e-book system are examined and discussed..
15. Fumiya Okubo, Takashi Yokomori, Decomposition and factorization of chemical reaction transducers, Theoretical Computer Science, 10.1016/j.tcs.2019.01.032, 777, 431-442, 2019.07, © 2019 Elsevier B.V. Chemical reaction automata, computing models inspired by chemical reactions occurring in nature, have been proposed and investigated in [28]. In this paper, we introduce the notion of a chemical reaction transducer (CRT) which is defined as a chemical reaction automaton equipped with output device. We investigate the problem of decomposing CRTs into simpler component CRTs in two different forms: serial decomposition and factorization. For the serial decomposition, we give a sufficient condition for CRTs to be serially decomposable. For factorization, we show that each CRT T can be realized in the form: T(x)=g(h−1(x)∩L) for some codings g,h and a chemical reaction language L, which provides a generalization of notable Nivat's Theorem for rational transducers. This result is then elaborated in a refined form. Further, some transformational characterizations of CRTs are also discussed..
16. 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, Integrating Multimodal Learning Analytics and Inclusive Learning Support Systems for People of All Ages, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10.1007/978-3-030-22580-3_35, 11577 LNCS, 469-481, 2019.06, © 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..
17. Chengjiu Yin, Masanori Yamada, Misato Oi, Atsushi Shimada, Fumiya Okubo, Kentaro Kojima, Hiroaki Ogata, Exploring the Relationships between Reading Behavior Patterns and Learning Outcomes Based on Log Data from E-Books: A Human Factor Approach, International Journal of Human-Computer Interaction, 10.1080/10447318.2018.1543077, 35, 4-5, 313-322, 2019.03, © 2018, © 2018 Taylor & Francis Group, LLC. Online learning environments presently accumulate large amounts of log data. Analysis of learning behaviors from these log data is expected to benefit instructors and learners. This study was intended to identify effective measures from e-book materials used at Kyushu University and to employ these measures for analyzing learning behavioral patterns. In an evaluation, students were grouped into four clusters using k-means clustering, and their learning behavioral patterns were analyzed. We examined whether the learning behavioral patterns exhibited relations with the learning outcomes. The results reveal that the learning behavior of “backtrack” style reading exerts a significant positive influence on learning effectiveness, which can aid students to learn more efficiently..
18. Li Chen, Hirokazu Uemura, Hao Hao, Yoshiko Goda, Fumiya Okubo, Yuta Taniguchi, Misato Oi, Shin'ichi Konomi, Hiroaki Ogata, Masanori Yamada, Relationships between Collaborative Problem Solving, Learning Performance and Learning Behavior in Science Education, Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018, 10.1109/TALE.2018.8615254, 17-24, 2019.01, © 2018 IEEE. This study was designed to identify correlations between students' awareness of Collaborative Problem Solving (CPS) and learning performance and learning behavior in science education. The topic of the course was Genetic Diseases which was implemented in a twelfth-grade class. To assess the effectiveness of this instructional design, and to find out potential factors that affect the using of CPS skills, multiple data sources including learning test scores, questionnaire feedback, and learning logs were analyzed. First, results indicated significant improvements in students' knowledge acquisition and awareness of Participation and Learning and Knowledge Building in CPS during the course. Furthermore, when we investigated the correlations between CPS awareness and learning performance and learning behavior, the findings indicated significant positive correlations between students' learning motivation and their awareness of CPS processes. However, there were negative correlations found between certain learning behavior factors with CPS awareness and learning motivation factors respectively..
19. Takashi Yokomori, Fumiya Okubo, Computing with multisets: A survey on reaction automata theory, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10.1007/978-3-319-94418-0_42, 10936 LNCS, 421-431, 2018.08, © 2018, Springer International Publishing AG, part of Springer Nature. In Natural Computing research [18], as mathematical modeling tools of biochemical reactions, Ehrenfeucht and Rozenberg introduced a formal model called reaction systems [6] for investigating the functioning of the living cell, where two basic components (reactants and inhibitors) play a key role as a regulation mechanism in controlling interactions..
20. Shin’ichi Konomi, Kohei Hatano, Miyuki Inaba, Misato Oi, Tsuyoshi Okamoto, Fumiya Okubo, Atsushi Shimada, Jingyun Wang, Masanori Yamada, Yuki Yamada, Towards supporting multigenerational co-creation and social activities: Extending learning analytics platforms and beyond, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10.1007/978-3-319-91131-1_6, 10922 LNCS, 82-91, 2018.07, © Springer International Publishing AG, part of Springer Nature 2018. As smart technologies pervade our everyday environments, they change what people should learn to live meaningfully as valuable participants of our society. For instance, ubiquitous availability of smart devices and communication networks may have reduced the burden for people to remember factual information. At the same time, they may have increased the benefits to master the uses of new digital technologies. In the midst of such a social and technological shift, we could design novel integrated platforms that support people at all ages to learn, work, collaborate, and co-create easily. In this paper, we discuss our ideas and first steps towards building an extended learning analytics platform that elderly people and unskilled adults can use. By understanding the characteristics and needs of elderly learners and addressing critical user interface issues, we can build pervasive and inclusive learning analytics platforms that trigger contextual reminders to support people at all ages to live and learn actively regardless of age-related differences of cognitive capabilities. We discuss that resolving critical usability problems for elderly people could open up a plethora of opportunities for them to search and exploit vast amount of information to achieve various goals..
21. Atsushi Shimada, Fumiya Okubo, Chengjiu Yin, Hiroaki Ogata, Automatic Summarization of Lecture Slides for Enhanced Student Preview-Technical Report and User Study, IEEE Transactions on Learning Technologies, 10.1109/TLT.2017.2682086, 11, 2, 165-178, 2018.04, © 2008-2011 IEEE. This paper is an extension of research originally reported in [1]. Here, we propose a novel method for summarizing lecture slides to enhance students' preview efficiency and understanding of the content. Students are often asked to prepare for a class by reading lecture materials. However, because the attention span of students is limited, this is not always beneficial. We surveyed 326 students regarding the preview of lecture materials, revealing a preference for summarized materials to preview. Therefore, we developed an automatic summarization method for condensing original lecture materials into a summarized set. Our proposed approach utilizes image and text processing to extract important pages from lecture materials, optimizing selection of pages in accordance with a specified preview time. We applied the proposed summarization method to a set of lecture slides. In an experiment with 372 students, we compared the effectiveness of the summarized slides and the original materials in terms of quiz scores, preview achievement ratio, and time spent previewing. We found that students who previewed the summarized slides achieved better scores on pre-lecture quizzes, even though they spent less time previewing the material..
22. Atsushi Shimada, Yuta Taniguchi, Fumiya Okubo, Shin’ichi Konomi, Hiroaki Ogata, Online change detection for monitoring individual student behavior via clickstream data on E-book system, ACM International Conference Proceeding Series, 10.1145/3170358.3170412, 446-450, 2018.03, © 2018 Association for Computing Machinery. We propose a new change detection method using clickstream data collected through an e-Book system. Most of the prior work has focused on the batch processing of clickstream data. In contrast, the proposed method is designed for online processing, with the model parameters for change detection updated sequentially based on observations of new click events. More specifically, our method generates a model for an individual student and performs minute-by-minute change detection based on click events during a classroom lecture. We collected clickstream data from four face-to-face lectures, and conducted experiments to demonstrate how the proposed method discovered change points and how such change points correlated with the students’ performances..
23. Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada, Yuta Taniguchi, Konomi Shin’ichi, On the prediction of students’ quiz score by recurrent neural network, CEUR Workshop Proceedings, 2163, 2018.03, © 2018 CEUR-WS. All Rights Reserved. In this paper, we explore the factor for improving the performance of prediction of students’ quiz scores by using a Recurrent Neural Network. The proposed method is applied to the log data of 2693 students in 15 courses that were conducted with following the common syllabus by 10 teachers. The experimental results show that in the case where the same teacher is not included in both training and test data, the accuracy of prediction slightly lower. We also show that at the beginning of a course, it is better to construct a prediction model including various items of learning logs, however, in the latter half, it is better to update the model by using selected information only..
24. Masanori Yamada, Atsushi Shimada, Fumiya Okubo, Misato Oi, Kentaro Kojima, Hiroaki Ogata, Learning analytics of the relationships among self-regulated learning, learning behaviors, and learning performance, Research and Practice in Technology Enhanced Learning, 10.1186/s41039-017-0053-9, 12, 1, 2017.12, © 2017, The Author(s). This research aims to investigate the relationship between self-regulated learning awareness, learning behaviors, and learning performance in ubiquitous learning environments. In order to conduct this research, psychometric data about self-regulated learning and log data, such as slide pages that learners read, marker, and annotate, was collected. The accessing activity of device types that stored the learning management system was collected and analyzed by applying path analysis and correlation analysis using data divided into high and low performers. The results indicated that the slide pages which learners read for a duration of between 240 and 299 s had positive effects on the promotion of annotation and the learning performance directly, and albeit indirectly, the enhancement of self-efficacy was affected by other self-regulated learning factors. The results of the correlation analysis indicated that self-efficacy and test anxiety are a key factor that has different effects on the number of the read slide pages in both high and low performers..
25. Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada, Shin'ichi Konomi, Analysis on students' usage of highlighters on e-textbooks in classroom, Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings, 514-516, 2017.12, © 2017 Asia-Pacific Society for Computers in Education. All rights reserved. E-book has been gradually getting popularity in educational contexts. Reading textbooks on computers or hand-held devices enables us to track the learning activities of students regardless of situations. In our university, several courses for first year students employs our e-book system, and we have been collecting its usage logs. From the logs, it seems that the highlighter function of the e-book reader plays an important role in learning because it is used most by the students. Though many researches studied the effectiveness of e-textbooks, only limited studies addressed how students utilize highlighters and how marking activity affects their learning. In this paper, we focus on highlighted portions of e-textbooks, and analyze how students use highlighters in their learning. We also attempt to provide recommendations to students for highlighting based on the highlighter usage in other classes..
26. Misato Oi, Fumiya Okubo, Yuta Taniguchi, Masanori Yamada, Shin'ichi Konomi, Effects of prior knowledge of high achievers on use of e-book highlights and annotations, Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings, 682-687, 2017.12, © 2017 Asia-Pacific Society for Computers in Education. All rights reserved. To identify “good performance,” this study analyzed the highlighting and annotating action logs of undergraduates during their e-book usage. To reveal “good performance,” the study focused on the learning behavior of high achieving students. Few highlights and annotations were observed for both rich knowledge and poor knowledge high achievers. Moreover, in the spontaneous usage of e-books outside the classroom, high and poor knowledge students did not display differences in highlights and annotations..
27. Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada, Shin'ichi Konomi, Students' performance prediction using data of multiple courses by recurrent neural network, Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings, 439-444, 2017.12, © 2017 Asia-Pacific Society for Computers in Education. All rights reserved. In this paper, we show a method to predict students' final grades using a recurrent neural network (RNN). An RNN is a variant of a neural network that handles time series data. For this purpose, the learning logs from 937 students who attended one of six courses by two teachers were collected. Nine kinds of learning logs are selected as the input of the RNN. We examine the prediction of final grades, where the training data and test data are the logs of courses conducted in 2015 and in 2016, respectively. We also show a way to identify the important learning activities for obtaining a specific final grade by observing the values of weight of the trained RNN..
28. Fumiya Okubo, Atsushi Shimada, Yuta Taniguchi, Shin'Ichi Konomi, A visualization system for predicting learning activities using state transition graphs, 14th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2017, 173-180, 2017.10, © 2017. In this paper, we present a system for visualizing learning logs of a course in progress together with predictions of learning activities of the following week and the final grades of students by state transition graphs. Data are collected from 236 students attending the course in progress and from 209 students attending the past course for prediction. From these data, the system constructs a state transition graph, where the prediction is based on the Markov property. We verify the performance of predictions by experiments in which the accuracy of prediction using the data of the course in progress and the one by 5-fold cross validation..
29. Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada, Shin'Ichi Konomi, Exploring students' learning journals with web-based interactive report tool, 14th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2017, 251-254, 2017.10, © 2017. Students' journal writings could be useful resources for teachers to grasp their understandings and to see their own teaching objectively. However, reading a large number of journals thoroughly is not always realistic for teachers. Although various automatic analysis methods have been proposed to understand learning journals, they does not necessarily fit needs of teachers and tend to overlook minor opinions. In this paper, we propose an interactive report tool for exploring journal writings. Focusing on the efficiency of reading learning journals, it employs weekly keywords extracted from journals as entry points for journal sentences. It enables us to read journal sentences selectively. The tool also provides lists of most used adjectives from week to week, which is helpful for teachers to grasp the temporal variation of opinions through a semester. We conducted a preliminary questionnaire about the usefulness of the report tool targeting teachers of the course "Information Science" in our university. Most of them evaluated our tool positively although the number of answers were small..
30. Fumiya Okubo, Takashi Yokomori, Morphic characterizations of language families based on local and star languages, Fundamenta Informaticae, 10.3233/FI-2017-1569, 154, 1-4, 323-341, 2017.08, New morphic characterizations in the form of a noted Chomsky-Schützenberger theorem are established for the classes of regular languages, of context-free languages and of languages accepted by chemical reaction automata. Our results include the following: (i) Each λ-free regular language R can be expressed as R = h(Tk ∩ FR) for some 2-star language FR, an extended 2-star language Tk and a weak coding h. (ii) Each λ-free context-free language L can be expressed as L = h(Dn ∩ FL) for some 2-local language FL and a projection h. (iii) A language L is accepted by a chemical reaction automaton iff there exist a 2-local language FL and a weak coding h such that L = h(Bn ∩ FL), where Dn and Bn are a Dyck set and a partially balanced language defined over the n-letter alphabet, respectively. These characterizations improve or shed new light on the previous results..
31. Hiroaki Ogata, Misato Oi, Kousuke Mohri, Fumiya Okubo, Atsushi Shimada, Masanori Yamada, Jingyun Wang, Sachio Hirokawa, Learning analytics for E-book-based educational big data in higher education, Smart Sensors at the IoT Frontier, 10.1007/978-3-319-55345-0_13, 327-350, 2017.05.
32. Misato Oi, Yamada, M., Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata, Reproducibility of Findings from Educational Big Data: A Preliminary Study, Proceedings of the 7th International Learning Analytics & Knowledge Conference (LAK2017), 536-537, 2017.03.
33. Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada, Hiroaki Ogata, A Neural Network Approach for Students’ Performance Prediction, Proceedings of the 7th International Learning Analytics & Knowledge Conference (LAK2017), 598-599, 2017.03.
34. Hiroaki Ogata, Yuta Taniguchi, Daiki Suehiro, Atsushi Shimada, Misato Oi, Fumiya Okubo, Yamada, M., Kentaro Kojima, M2B System: A Digital Learning Platform for Traditional Classrooms in University, Practitioner Track Proceedings of the 7th International Learning Analytics & Knowledge Conference (LAK2017), 154-161, 2017.03.
35. Misato Oi, Masanori Yamada, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata, Finding traces of high and low achievers by analyzing undergraduates' e-book logs, CEUR Workshop Proceedings, 1828, 15-22, 2017.03, © 2017, CEUR-WS. All rights reserved. We investigated the learning behavior of undergraduates with e-book logs. E-book logs from 99 undergraduates taking an information science course were collected. First, we analyzed differences between nine high-achieving students and three low-achieving students. A log recorded before a class session in which the same e-book was used as a textbook was considered a preview log, and one recorded after a class session was considered a review log. The analysis of preview frequency indicates that the low achievers did not perform the previews, but many high achievers frequently did. The review frequency demonstrates that regardless of high and low achievements, students performed reviews. We added the logs of six relatively low achievers and analyzed more details of the preview logs of high and low achievers. The number of page flips and durations of preview logs revealed that relatively low achievers tried to perform previews, but they gave the endeavor up easily..
36. Yamada, M., Misato Oi, Fumiya Okubo, Atsushi Shimada, Kentaro Kojima, Hiroaki Ogata, Learning Analytics in Ubiquitous Learning Environments: Self-Regulated Learning Perspective, Proceedings of the 24th International Conference on Computers in Education (ICCE2016), 306-314, 2016.12.
37. Atsushi Shimada, Fumiya Okubo, Hiroaki Ogata, Browsing-Pattern Mining from e-Book Logs with Non-negative Matrix Factorization, Proceedings of the 9th International Conference on Educational Data Mining, 636-637, 2016.07.
38. Kousuke Mouri, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata, Bayesian Network for Predicting Students’ Final Grade using e-book Logs in University Education, Proceedings of the 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016), 85-89, 2016.07.
39. Fumiya Okubo, Takashi Yokomori, The Computational Capability of Chemical Reaction Automata, Natural Computing, 15, 2, 215-224, 2016.05.
40. 緒方 広明, 殷 成久, 毛利 考佑, 大井 京, 島田 敬士, 大久保 文哉, 山田政寛, 小島 健太郎, 教育ビッグデータの利活用に向けた学習ログの蓄積と分析, 教育システム情報学会誌, 33, 2, 58-66, 2016.05.
41. Kousuke Mouri, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata, Profiling High-achieving Students using E-book-based Logs, Proceedings of the 1st International Workshop on Learning Analytics Across Physical and Digital Spaces (Cross-LAK 2016), 5-9, 2016.04.
42. Atsushi Shimada, Fumiya Okubo, Chengjiu Yin, Hiroaki Ogata, Automatic Generation of Personalized Review Materials Based on Across-Learning-System Analysis, Proceedings of the 1st International Workshop on Learning Analytics Across Physical and Digital Spaces (Cross-LAK 2016), 22-27, 2016.04.
43. Fumiya Okubo, Sachio Hirokawa, Misato Oi, Atsushi Shimada, Kojima Kentaro, Yamada Masanori, Hiroaki Ogata, Learning Activity Features of High Performance Students, Proceedings of the 1st International Workshop on Learning Analytics Across Physical and Digital Spaces (Cross-LAK 2016), 28-33, 2016.04.
44. 緒方 広明, 殷 成久, 大井 京, 大久保 文哉, 島田 敬士, 小島 健太郎, 山田政寛, デジタル教材の閲覧ログを利用したアクティブ・ラーナーの学習行動の分析, 基幹教育紀要, 2, 48-60, 2016.03.
45. 山田政寛, 岡本 剛, 島田 敬士, 木村 拓也, 大久保 文哉, 小島 健太郎, 緒方 広明, eポートフォリオは省察に有効か? ポートフォリオの媒体の違いが学習者の主観的効果に与える影響の分析, 基幹教育紀要, 2, 61-72, 2016.03.
46. Misato Oi, CHENGJIU YIN, Fumiya Okubo, Atsushi Shimada, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata, Analysis of Links among E-books in Undergraduates’ E-Book Logs, Workshop Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015), 665-669, 2015.11.
47. CHENGJIU YIN, Fumiya Okubo, Atsushi Shimada, Misato Oi, Sachio Hirokawa, Masanori Yamada, Kojima Kentaro, Hiroaki Ogata, Analyzing the Features of Learning Behaviors of Students using e-Books, Workshop Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015), 617-626, 2015.11.
48. Atsushi Shimada, Fumiya Okubo, CHENGJIU YIN, Misato Oi, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata, Analysis of Preview Behavior in E-Book System, Workshop Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015), 593-600, 2015.11.
49. Fumiya Okubo, Atsushi Shimada, CHENGJIU YIN, Hiroaki Ogata, Visualization and Prediction of Learning Activities by Using Discrete Graphs, Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015), 739-744, 2015.11.
50. Hiroaki Ogata, CHENGJIU YIN, Misato Oi, Fumiya Okubo, Atsushi Shimada, Kojima Kentaro, Masanori Yamada, E‐Book‐based Learning Analytics in University Education, Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015), 401-406, 2015.11.
51. Atsushi Shimada, Fumiya Okubo, CHENGJIU YIN, Hiroaki Ogata, Automatic Summarization of Lecture Slides for Enhanced Student Preview, Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015), 218-227, 2015.11.
52. Misato Oi, Fumiya Okubo, Atsushi Shimada, CHENGJIU YIN, Hiroaki Ogata, Analysis of Preview and Review Patterns in Undergraduates’ E‐Book Logs, Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015), 166-171, 2015.11.
53. CHENGJIU YIN, Fumiya Okubo, Atsushi Shimada, Sachio Hirokawa, Misato Oi, Hiroaki Ogata, Identifying and Analyzing the Learning Behaviors of Students using e‐Books, Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015), 118-120, 2015.11.
54. Atsushi Shimada, Fumiya Okubo, CHENGJIU YIN, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata, Informal Learning Behavior Analysis Using Action Logs and Slide Features in E-textbooks, Proceedings of the 15th IEEE International Conference on Advanced Learning Technologies (ICALT 2015), 116-117, 2015.07.
55. Masanori Yamada, CHENGJIU YIN, Atsushi Shimada, Kojima Kentaro, Fumiya Okubo, Hiroaki Ogata, Preliminary Research on Self-regulated Learning and Learning Logs in a Ubiquitous Learning Environment, Proceedings of the 15th IEEE International Conference on Advanced Learning Technologies (ICALT 2015), 93-95, 2015.07.
56. Fumiya Okubo, Takashi Yokomori, Finite Automata with Multiset Memory: A New Characterization of Chomsky Hierarchy, Fundamenta Informaticae, 138, 31-44, 2015.04.
57. Fumiya Okubo, Takashi Yokomori, The Computational Capability of Chemical Reaction Automata, Lecture Notes in Computer Science, 8727, 53-66, 2014.09.
58. Fumiya Okubo, REACTION AUTOMATA WORKING IN SEQUENTIAL MANNER, RAIRO-THEORETICAL INFORMATICS AND APPLICATIONS, 10.1051/ita/2013047, 48, 1, 23-38, 2014.01.
59. Fumiya Okubo, On language classes defined by reaction automata, 早稲田大学教育学部 学術研究 自然科学編, 61, 39-46, 2013.03.
60. Fumiya Okubo, Satoshi Kobayashi, Takashi Yokomori, On the properties of language classes defined by bounded reaction automata, THEORETICAL COMPUTER SCIENCE, 10.1016/j.tcs.2012.03.024, 454, 206-221, 2012.10.
61. Fumiya Okubo, Satoshi Kobayashi, Takashi Yokomori, Automata inspired by biochemical reaction, 京都大学数理解析研究所講究録, 1799, 179-182, 2012.06.
62. Fumiya Okubo, Satoshi Kobayashi, Takashi Yokomori, Reaction automata, THEORETICAL COMPUTER SCIENCE, 10.1016/j.tcs.2011.12.045, 429, 247-257, 2012.04.
63. Fumiya Okubo, Takashi Yokomori, On the Hairpin Incompletion, FUNDAMENTA INFORMATICAE, 10.3233/FI-2011-542, 110, 1-4, 255-269, 2011.09.
64. Fumiya Okubo, Takashi Yokomori, MORPHIC CHARACTERIZATIONS OF LANGUAGE FAMILIES IN TERMS OF INSERTION SYSTEMS AND STAR LANGUAGES, INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE, 10.1142/S012905411100799X, 22, 1, 247-260, 2011.01.
65. Fumiya Okubo, A note on the descriptional complexity of semi-conditional grammars, INFORMATION PROCESSING LETTERS, 10.1016/j.ipl.2009.10.002, 110, 1, 36-40, 2009.12.

九大関連コンテンツ

pure2017年10月2日から、「九州大学研究者情報」を補完するデータベースとして、Elsevier社の「Pure」による研究業績の公開を開始しました。
 
 
九州大学知的財産本部「九州大学Seeds集」