Updated on 2024/10/08

Information

 

写真a

 
OKUBO FUMIYA
 
Organization
Faculty of Information Science and Electrical Engineering Department of Advanced Information Technology Associate Professor
Data-Driven Innovation Initiative (Concurrent)
Learning Analytics Center (Concurrent)
School of Engineering Department of Electrical Engineering and Computer Science(Concurrent)
Graduate School of Information Science and Electrical Engineering Department of Information Science and Technology(Concurrent)
Joint Graduate School of Mathematics for Innovation (Concurrent)
Title
Associate Professor
Contact information
メールアドレス
Profile
Natural Computing Leaning Analytics
External link

Research Areas

  • Informatics / Learning support system

  • Informatics / Theory of informatics

Degree

  • Ph.D of Science

Research History

  • Kyushu University Faculty of Information Science and Electrical Engineering Associate Professor 

    2021.4 - Present

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Research Interests・Research Keywords

  • Research theme: Learning Analytics

    Keyword: Learning Analytics

    Research period: 2024

  • Research theme: Natural Computing

    Keyword: Natural Computing

    Research period: 2024

  • Research theme: Theory of Computation

    Keyword: Theory of Computation

    Research period: 2024

  • Research theme: Learning Analytics

    Keyword: Learning Log, LMS, E-book

    Research period: 2015.10

  • Research theme: Computation model on biochemical reactions

    Keyword: natural computing, computation model, reaction automata

    Research period: 2011.4

Awards

  • ICALT2015 Best Paper Award(Short paper)

    2015.7   The 15th IEEE International Conference on Advanced Learning Technologies (ICALT 2015)  

  • 小野梓記念学術賞

    2014.3   早稲田大学  

Papers

  • The Computational Capability of Chemical Reaction Automata Invited Reviewed International journal

    Fumiya Okubo, Takashi Yokomori

    Natural Computing   15 ( 2 )   215 - 224   2016.5

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

  • Reaction automata Reviewed International journal

    Fumiya Okubo, Satoshi Kobayashi, Takashi Yokomori

    THEORETICAL COMPUTER SCIENCE   429   247 - 257   2012.4

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

    DOI: 10.1016/j.tcs.2011.12.045

  • On the Hairpin Incompletion Reviewed International journal

    Fumiya Okubo, Takashi Yokomori

    FUNDAMENTA INFORMATICAE   110 ( 1-4 )   255 - 269   2011.9

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

    DOI: 10.3233/FI-2011-542

  • MORPHIC CHARACTERIZATIONS OF LANGUAGE FAMILIES IN TERMS OF INSERTION SYSTEMS AND STAR LANGUAGES Reviewed International journal

    Fumiya Okubo, Takashi Yokomori

    INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE   22 ( 1 )   247 - 260   2011.1

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

    DOI: 10.1142/S012905411100799X

  • A note on the descriptional complexity of semi-conditional grammars Reviewed International journal

    Fumiya Okubo

    INFORMATION PROCESSING LETTERS   110 ( 1 )   36 - 40   2009.12

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

    DOI: 10.1016/j.ipl.2009.10.002

  • Educational Data Analysis using Generative AI Reviewed International journal

    Abdul Berr, Sukrit Leelaluk, Cheng Tang, Li Chen, Fumiya Okubo, Atsushi Shimada

    The 6th Workshop on Predicting Performance Based on the Analysis of Reading Behavior (LAK24 Data Challenge)   2024.3

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    Language:English   Publishing type:Research paper (international conference proceedings)  

  • A Deep learning Grade Prediction Model of Online Learning Performance Based on knowledge learning representation Reviewed International journal

    Shuaileng Yuan, Sukrit Leelaluk, Cheng Tang, Li Chen, Fumiya Okubo, Atsushi Shimada

    The 6th Workshop on Predicting Performance Based on the Analysis of Reading Behavior (LAK24 Data Challenge)   2024.3

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    Language:English   Publishing type:Research paper (international conference proceedings)  

  • A Human-in-the-Loop Annotation Framework for Surveillance Scenarios with Enhanced Overlapping Object Detection Reviewed International journal

    Dao Zhou, Tsubasa Minematsu, Cheng Tang, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    The 30th International Workshop on Frontiers of Computer Vision 2024 (IW-FCV2024)   2024.2

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    Language:English   Publishing type:Research paper (international conference proceedings)  

  • QA-Knowledge Attention for Exam Performance Prediction

    Ren Y., Tang C., Taniguchi Y., Okubo F., Shimada A.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   15159 LNCS   375 - 389   2024   ISSN:03029743 ISBN:9783031723148

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    Publisher:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

    In actual university education, students’ performance prediction is important for assessing their mastery of specific knowledge areas and providing feedback. To address the limitations of existing grade or at-risk predictions that fail to take students’ knowledge mastery into account, we propose a model, question&answer knowledge attention for exam performance prediction (QAKAP) and focus on predicting students’ performance on each final exam question (binary prediction). This model consists of three key modules: a Topic-Aware Attention Module, a Knowledge Mastery Estimation Module, and a Prediction Module. By integrating natural language processing (NLP) methods and the attention mechanism, our model can capture the relevance of questions from question text data and then generate the student embeddings to predict student performance on each final exam question. The primary contribution of this work is the development of a general prediction model for real university education, applicable to students’ exam performance predictions. We collected a dataset from 494 students across four undergraduate courses, including the question text data and students’ performance on these exam questions. The results show that QAKAP outperforms other machine-learning methods, demonstrating its effectiveness in predicting students’ exam performance.

    DOI: 10.1007/978-3-031-72315-5_26

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  • Visual Analytics of Learning Behavior Based on the Dendritic Neuron Model

    Cheng Tang, Li Chen, Gen Li, Tsubasa Minematsu, Fumiya Okubo, Yuta Taniguchi, Atsushi Shimada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   14885   192 - 203   2024   ISSN:2945-9133 ISBN:978-981-97-5494-6 eISSN:1611-3349

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    Publishing type:Research paper (international conference proceedings)   Publisher:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

    Learning analytics, blending education theory, psychology, statistics, and computer science, utilizes data about learners and their environments to enhance education. Artificial Intelligence advances this field by personalizing learning and providing predictive insights. However, the opaque ’black box’ nature of AI decision-making poses challenges to trust and understanding within educational settings. This paper presents a novel visual analytics method to predict whether a student is at risk of failing a course. The proposed method is based on a dendritic neuron model (DNM), which not only performs excellently in prediction, but also provides an intuitive visual presentation of the importance of learning behaviors. It is worth emphasizing that the proposed DNM has a better performance than recurrent neural network (RNN), long short term memory network (LSTM), gated recurrent unit (GRU), bidirectional long short term memory network (BiLSTM) and bidirectional gated recurrent unit (BiGRU). The powerful prediction performance can assist instructors in identifying students at risk of failing and performing early interventions. The importance analysis of learning behaviors can guide students in the development of learning plans.

    DOI: 10.1007/978-981-97-5495-3_14

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  • How Do Strategies for Using ChatGPT Affect Knowledge Comprehension?

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

    Communications in Computer and Information Science   2150 CCIS   151 - 162   2024   ISSN:1865-0929 ISBN:9783031643149 eISSN:1865-0937

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    Publishing type:Research paper (international conference proceedings)   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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  • Educational Data Analysis using Generative AI

    Berr A., Leelaluk S., Tang C., Chen L., Okubo F., Shimada A.

    CEUR Workshop Proceedings   3667   47 - 55   2024   ISSN:16130073

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    Publisher:CEUR Workshop Proceedings  

    With the advent of generative artificial intelligence (AI), the scope of data analysis, prediction of performances, real-time feedback, etc. in learning analytics has widened. The purpose of this study is to explore the possibility of using generative AI to analyze educational data. Moreover, the performances of two large language models (LLMs): GPT-4 and text-davinci-003, are compared with respect to different types of analyses. Additionally, a framework, LangChain, is integrated with the LLM in order to achieve deeper insights into the analysis, which can be beneficial for beginner data scientists. LangChain has a component called an agent, which can help study the analysis being performed step-by-step. Furthermore, the impact of the OpenLA library, which pre-processes the data by calculating the number of reading seconds of students, counting the number of operations performed by students, and making page-wise behavior of each student, is also studied. Besides, factors with the most significant impact on students’ performances were also discovered in this analysis. The results show that GPT-4, when using the data pre-processed by OpenLA, provides the best analysis in terms of both, the accuracy of the final answer, and the step-by-step insights provided by LangChain’s agent. Also, we learn the significance of reading time and interactions used (Add marker, bookmark, memo) by students in predicting grades.

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  • Educational Data Analysis using Generative AI.

    Abdul Berr, Sukrit Leelaluk, Cheng Tang, Li Chen, Fumiya Okubo, Atsushi Shimada

    LAK Workshops   47 - 55   2024

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

  • A Deep learning Grade Prediction Model of Online Learning Performance Based on knowledge learning representation

    Shuaileng Yuan, Sukrit Leelaluk, Cheng Tang, Li Chen, Fumiya Okubo, Atsushi Shimada

    CEUR Workshop Proceedings   3667   73 - 82   2024   ISSN:1613-0073

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    In recent years, due to the impact of Coronavirus disease (COVID-19), digital platforms have developed rapidly and accumulated a large amount of data. To better utilize the comprehensive and diverse data stored in online platforms for data mining, such as learning behavior analysis or performance prediction, and to provide guidance and valuable feedback for educator became more important. For the current analysis of learning behaviors by time series data with DNN method, the interpretability is not enough. This paper proposes a method based on the simultaneous use of learning behaviors and learning materials to obtain the representation of learned knowledge, and through multiple cross-validations, the effect of this knowledge representation has a certain improvement on the original data, and the interpretability can promote the feedback function.

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  • A Deep learning Grade Prediction Model of Online Learning Performance Based on knowledge learning representation

    Yuan S., Leelaluk S., Tang C., Chen L., Okubo F., Shimada A.

    CEUR Workshop Proceedings   3667   73 - 82   2024   ISSN:16130073

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    Publisher:CEUR Workshop Proceedings  

    In recent years, due to the impact of Coronavirus disease (COVID-19), digital platforms have developed rapidly and accumulated a large amount of data. To better utilize the comprehensive and diverse data stored in online platforms for data mining, such as learning behavior analysis or performance prediction, and to provide guidance and valuable feedback for educator became more important. For the current analysis of learning behaviors by time series data with DNN method, the interpretability is not enough. This paper proposes a method based on the simultaneous use of learning behaviors and learning materials to obtain the representation of learned knowledge, and through multiple cross-validations, the effect of this knowledge representation has a certain improvement on the original data, and the interpretability can promote the feedback function.

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  • Attention-Based Artificial Neural Network for Student Performance Prediction Based on Learning Activities

    Sukrit Leelaluk, Cheng Tang, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada

    IEEE Access   12   100659 - 100675   2024   ISSN:2169-3536 eISSN:2169-3536

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    Publishing type:Research paper (scientific journal)   Publisher:IEEE Access  

    Student performance prediction was deployed to predict learning performance to identify at-risk students and provide interventions for them. However, prediction models should also consider external factors along with learning activities, such as course duration. Thus, we aim to distinguish the difference factor between the time dimension (duration of the course) and the feature dimension (students' learning activities) by attention weights to provide helpful information and improve predictions of student performance. In this study, we introduce Attention-Based Artificial Neural Network (Attn-ANN), a novel model in educational data mining. The Attn-ANN combines attention weighting on the time and feature dimensions to examine the significance of lectures and learning activities and makes predictions by visualizing attention weight. We found that the Attn-ANN had a better area under the curve scores than conventional algorithms, and the attention mechanism allowed models to focus on input selectively. Incorporating the attention weighting of both the time and feature dimensions improved the prediction performance in an ablation study. Finally, we investigated and analyzed the model's decision, finding that the Attn-ANN may be able to create synergy in real-world scenarios between the Attn-ANN's predictions and instructors' expertise, which underscores a novel contribution to engineering applications for interventions for at-risk students.

    DOI: 10.1109/ACCESS.2024.3429554

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  • Investigating Programming Performance Predictability from Embedding Vectors of Coding Behaviors

    Ikkei Igawa, Yuta Taniguchi, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada

    31st International Conference on Computers in Education, ICCE 2023 - Proceedings   1   487 - 489   2023.12

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

    Understanding students' coding behaviors is crucial for providing targeted support in programming education. Automatic analysis of coding behaviors using machines can address the limitations of manual monitoring. Previous studies focused on coding behavior representations without considering differences relative to a model answer. We propose embedding vectors that capture these differences, enabling the distinction between simple and complex code solutions. Evaluating these vectors by predicting assignment scores, we achieved over 15% higher accuracy compared to conventional methods. This approach has the potential to enhance teachers' understanding of students' coding behaviors and improve support in programming education.

  • Investigating Programming Performance Predictability from Embedding Vectors of Coding Behaviors

    Igawa I., Taniguchi Y., Minematsu T., Okubo F., Shimada A.

    31st International Conference on Computers in Education, ICCE 2023 - Proceedings   1   487 - 489   2023.12   ISBN:9786269689019

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    Publisher:31st International Conference on Computers in Education, ICCE 2023 - Proceedings  

    Understanding students' coding behaviors is crucial for providing targeted support in programming education. Automatic analysis of coding behaviors using machines can address the limitations of manual monitoring. Previous studies focused on coding behavior representations without considering differences relative to a model answer. We propose embedding vectors that capture these differences, enabling the distinction between simple and complex code solutions. Evaluating these vectors by predicting assignment scores, we achieved over 15% higher accuracy compared to conventional methods. This approach has the potential to enhance teachers' understanding of students' coding behaviors and improve support in programming education.

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  • Investigating Programming Performance Predictability from Embedding Vectors of Coding Behaviors

    Ikkei Igawa, Yuta Taniguchi, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada

    31st International Conference on Computers in Education, ICCE 2023 - Proceedings   1   487 - 489   2023.12   ISBN:9786269689019

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    Publishing type:Research paper (international conference proceedings)  

    Understanding students' coding behaviors is crucial for providing targeted support in programming education. Automatic analysis of coding behaviors using machines can address the limitations of manual monitoring. Previous studies focused on coding behavior representations without considering differences relative to a model answer. We propose embedding vectors that capture these differences, enabling the distinction between simple and complex code solutions. Evaluating these vectors by predicting assignment scores, we achieved over 15% higher accuracy compared to conventional methods. This approach has the potential to enhance teachers' understanding of students' coding behaviors and improve support in programming education.

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  • Development and Evaluation of a Field Environment Digest System for Agricultural Education Reviewed International journal

    Kanu Shiga, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada, Rin-ichiro Taniguchi

    Towards a Collaborative Society Through Creative Learning   685   87 - 99   2023.9

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  • A System to Realize Time- and Location-Independent Teaching and Learning Among Learners Through Sharing Learning-Articles Reviewed International journal

    Seiyu Okai, Tsubasa Minematsu, Fumiya Okubo, Yuta Taniguchi, Hideaki Uchiyama, Atsushi Shimada

    Towards a Collaborative Society Through Creative Learning   685   475 - 487   2023.9

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  • Evaluation of appropriate conditions for Oncomine DxTT testing of FFPE specimens for driver gene alterations in non-small cell lung cancer

    Iwama, E; Yamamoto, H; Okubo, F; Ijichi, K; Ibusuki, R; Shiaraishi, Y; Yoneshima, Y; Tanaka, K; Oda, Y; Okamoto, I

    THORACIC CANCER   14 ( 23 )   2288 - 2296   2023.8   ISSN:1759-7706 eISSN:1759-7714

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  • Recurrent Neural Network-FitNets: Improving Early Prediction of Student Performanceby Time-Series Knowledge Distillation

    Ryusuke Murata, Fumiya Okubo, Tsubasa Minematsu, Yuta Taniguchi, Atsushi Shimada

    Journal of Educational Computing Research   61 ( 3 )   639 - 670   2023.6   ISSN:0735-6331 eISSN:1541-4140

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    Language:Others   Publishing type:Research paper (scientific journal)   Publisher:Journal of Educational Computing Research  

    This study helps improve the early prediction of student performance by RNN-FitNets, which applies knowledge distillation (KD) to the time series direction of the recurrent neural network (RNN) model. The RNN-FitNets replaces the teacher model in KD with “an RNN model with a long-term time-series in which the features during the entire course are inputted” and the student model in KD with “an RNN model with a short-term time-series in which only the features during the early stages are inputted.” As a result, the RNN model in the early stage was trained to output the same results as the more accurate RNN model in the later stages. The experiment compared RNN-FitNets with a normal RNN model on a dataset of 296 university students in total. The results showed that RNN-FitNets can improve early prediction. Moreover, the SHAP value was employed to explain the contribution of the input features to the prediction results by RNN-FitNets. It was shown that RNN-FitNets can consider the future effects of the input features from the early stages of the course.

    DOI: 10.1177/07356331221129765

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  • Adaptive Learning Support System Based on Automatic Recommendation of Personalized Review Materials

    Fumiya Okubo, Tetsuya Shiino, Tsubasa Minematsu, Yuta Taniguchi, Atsushi Shimada

    IEEE Transactions on Learning Technologies   16 ( 1 )   92 - 105   2023.2   ISSN:1939-1382 eISSN:1939-1382

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    Language:Others   Publishing type:Research paper (scientific journal)   Publisher:IEEE Transactions on Learning Technologies  

    In this study, we propose an integrated system to support learners' reviews. In the proposed system, the review dashboard is used to recommend review contents that are adaptive to the individual learner's level of understanding and to present other information that is useful for review. The pages of the digital learning materials that are estimated to be insufficiently understood by each learner and the webpages related to those pages are recommended. As a method for estimating such pages, we consider extracting the pages related to the questions that were answered incorrectly. We examined the accuracy of matching each question with the pages of the learning materials. We also conducted an experiment to verify the usefulness of the system and its effect on learning using a review dashboard. In the experiment, the evaluation of the review dashboard indicated that at least half of the participants found it useful for most types of feedback. In addition, the rate of change in quiz scores was significantly higher in the group using the review dashboard, which indicates that using the review dashboard has the effect of improving learning.

    DOI: 10.1109/TLT.2022.3225206

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  • LECTOR: An attention-based model to quantify e-book lecture slides and topics relationships.

    Erwin D. López Z., Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    EDM   2023

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    Other Link: https://dblp.uni-trier.de/rec/conf/edm/2023

  • Investigating Programming Performance Predictability from Embedding Vectors of Coding Behaviors

    Igawa, I; Taniguchi, Y; Minematsu, T; Okubo, F; Shimada, A

    31ST INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION, ICCE 2023, VOL I   487 - 489   2023   ISBN:978-626-968-901-9

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  • A System to Realize Time- and Location-Independent Teaching and Learning Among Learners Through Sharing Learning-Articles

    Seiyu Okai, Tsubasa Minematsu, Fumiya Okubo, Yuta Taniguchi, Hideaki Uchiyama, Atsushi Shimada

    IFIP Advances in Information and Communication Technology   685 AICT   475 - 487   2023   ISSN:1868-4238 ISBN:9783031433924 eISSN:1868-422X

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    Publishing type:Research paper (international conference proceedings)   Publisher:IFIP Advances in Information and Communication Technology  

    Teaching and learning from one another is one of the most effective ways for learners to acquire proactive learning attitudes. In this study, we propose a new learning support system that encourages mutual teaching and learning by introducing a mechanism that guarantees sustainability. Learners submit articles called “learning-articles” that summarize their own learning and knowledge. The proposed system not only accumulates and publishes these articles but also has a mechanism to encourage the submission of necessary topics. The proposed system has been in operation since the academic year 2020, and it has collected learning-articles across our university’s nine academic disciplines from more than 300 learners. To investigate the effects of sharing learning-articles on education from the learners’ perspectives, a questionnaire was distributed among 25 students.

    DOI: 10.1007/978-3-031-43393-1_44

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  • Development and Evaluation of a Field Environment Digest System for Agricultural Education

    Kanu Shiga, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada, Rin ichiro Taniguchi

    IFIP Advances in Information and Communication Technology   685 AICT   87 - 99   2023   ISSN:1868-4238 ISBN:9783031433924 eISSN:1868-422X

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    Publishing type:Research paper (international conference proceedings)   Publisher:IFIP Advances in Information and Communication Technology  

    Smart agriculture has assumed increasing importance due to the growing age of farmers and a shortage of farm leaders. In response, it is crucial to provide more opportunities to learn about smart agriculture at agricultural colleges and high schools, where new farmers are trained. In agricultural education, a system is used for managing environmental information, such as temperature and humidity, obtained from sensors installed in the field. However, it is difficult to make effective use of this system due to the time required to detect changes in the field interfering with class time and the problem of oversight. In this study, we proposed a field environment digest system that will help learners by providing the summarized field sensing information, and support them in analyzing the data. In addition, to examine the potential for using field sensing information in agricultural education, we investigated the usefulness of the summarized sensor information and students’ usage of this information. In this paper, we outline the contents of the developed system and the results of the digest evaluation experiments.

    DOI: 10.1007/978-3-031-43393-1_10

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  • Topic-Based Representation of Learning Activities for New Learning Pattern Analytics

    Jinghao Wang, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings   1   268 - 278   2022.11   ISBN:9789869721493

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    In recent years, several kinds of e-learning systems, such as e-book and Learning Management System (LMS) have been widely used in the field of education. When students access these systems, their activities on the systems will be continuously and automatically recorded and stored as learning logs. As the learning logs are stored in association with students and indicate students' learning activities, most studies have been “student-based” learning log analyses focused on students and each student's learning behavior. However, the “student-based” learning log analysis focuses on each student's learning behavior during the entire lesson (for example, studied well or didn't study enough) and cannot show what they learned. Therefore, if there is a need to investigate students' learning behavior regarding each topic of the lesson, such as which topic is learned well and which not in order to optimize the syllabus, we cannot conduct “student-based” learning log analysis directly. Instead of “student-based” learning log analyses, this study describes a method of “learning-topic-based” learning log analysis. We will show how to convert a learning log associated with students into a learning-topic-associated one and shape the logs into a two-dimensional matrix of learning topics and learning activities. Then we apply Non-negative Matrix Factorization (NMF) to the matrix in order to extract the learning patterns by activity. In addition, we make a three-dimensional matrix (tensor) of students, learning topics, and learning activities by subdividing the learning activities of each learning topic by students. We then apply Non-negative Tensor Factorization (NTF) to the tensor to extract detailed learning patterns. The methods proposed in this study will help teachers to have a comprehensively view of students' learning behaviors towards each learning topic easily even if the learning log is in a large-scale, so teachers can adjust syllabus according to the attracted learning behaviors, which is helpful to increase learning efficiency.

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  • Topic-Based Representation of Learning Activities for New Learning Pattern Analytics

    Wang J., Minematsu T., Taniguchi Y., Okubo F., Shimada A.

    30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings   1   268 - 278   2022.11   ISBN:9789869721493

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    Publisher:30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings  

    In recent years, several kinds of e-learning systems, such as e-book and Learning Management System (LMS) have been widely used in the field of education. When students access these systems, their activities on the systems will be continuously and automatically recorded and stored as learning logs. As the learning logs are stored in association with students and indicate students' learning activities, most studies have been “student-based” learning log analyses focused on students and each student's learning behavior. However, the “student-based” learning log analysis focuses on each student's learning behavior during the entire lesson (for example, studied well or didn't study enough) and cannot show what they learned. Therefore, if there is a need to investigate students' learning behavior regarding each topic of the lesson, such as which topic is learned well and which not in order to optimize the syllabus, we cannot conduct “student-based” learning log analysis directly. Instead of “student-based” learning log analyses, this study describes a method of “learning-topic-based” learning log analysis. We will show how to convert a learning log associated with students into a learning-topic-associated one and shape the logs into a two-dimensional matrix of learning topics and learning activities. Then we apply Non-negative Matrix Factorization (NMF) to the matrix in order to extract the learning patterns by activity. In addition, we make a three-dimensional matrix (tensor) of students, learning topics, and learning activities by subdividing the learning activities of each learning topic by students. We then apply Non-negative Tensor Factorization (NTF) to the tensor to extract detailed learning patterns. The methods proposed in this study will help teachers to have a comprehensively view of students' learning behaviors towards each learning topic easily even if the learning log is in a large-scale, so teachers can adjust syllabus according to the attracted learning behaviors, which is helpful to increase learning efficiency.

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  • Detection of At-Risk Students in Programming Courses

    Ikkei Igawa, Yuta Taniguchi, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada

    30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings   1   308 - 313   2022.11   ISBN:9789869721493

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    Since the demand for programmers is increasing, programming courses are being offered widely. In this context, students' motivation can be damaged by difficulties they encounter in their programming courses. Although teachers' support is necessary to prevent such an issue, it is impossible for teachers to directly monitor all students' programming activities at the same time and determine which students have troubles with programming. Therefore, several studies have been conducted to help teachers monitor students. However, these studies do not provide an understanding of the activities of students who do not run their code, which may lead researchers to miss students who are in trouble. In this paper, we propose an indicator for detecting students who need coding support by analyzing programming logs that are recorded even when the students do not run their code. This gives teachers deeper insight into the students' programming performance. Although further work remains, the validation of this indicator shows that it could detect those students who are in trouble.

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  • Assessment of At-Risk Students' Predictions From e-Book Activities Representations in Practical Applications

    Lopez Z E.D., Minematsu T., Taniguchi Y., Okubo F., Shimada A.

    30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings   1   279 - 288   2022.11   ISBN:9789869721493

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    The use of e-book reading systems such as Bookroll, and their ability to record readers' activities allows the design of predictive models capable of identifying at-risk students from their reading characteristics. Even though previous works have obtained promising results in this task, these results may not evidence the expected prediction performance in practical applications due to their selected assessment methods. Accordingly, in this paper, we assess this performance in two practical scenarios. The first is when we keep stored data from previous years of our course which can be used to train our model, and the second is when we only have data from a different course to use in this training process. In order to obtain a more accurate assessment, we collected 92, 574 samples of predictive performances from different models under the above-mentioned conditions. We also considered different feature representations along with variational latent representations, which can leverage our previous data to automatically design general hidden features. From our results, we understand that in the first condition we can expect a relatively good predictive performance, especially when using variational latent representations. However, in the second condition we found that even when using them, the predictive performances are very limited resulting in an impractical solution.

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  • Assessment of At-Risk Students' Predictions From e-Book Activities Representations in Practical Applications

    Erwin D. Lopez Z, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings   1   279 - 288   2022.11   ISBN:9789869721493

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    The use of e-book reading systems such as Bookroll, and their ability to record readers' activities allows the design of predictive models capable of identifying at-risk students from their reading characteristics. Even though previous works have obtained promising results in this task, these results may not evidence the expected prediction performance in practical applications due to their selected assessment methods. Accordingly, in this paper, we assess this performance in two practical scenarios. The first is when we keep stored data from previous years of our course which can be used to train our model, and the second is when we only have data from a different course to use in this training process. In order to obtain a more accurate assessment, we collected 92, 574 samples of predictive performances from different models under the above-mentioned conditions. We also considered different feature representations along with variational latent representations, which can leverage our previous data to automatically design general hidden features. From our results, we understand that in the first condition we can expect a relatively good predictive performance, especially when using variational latent representations. However, in the second condition we found that even when using them, the predictive performances are very limited resulting in an impractical solution.

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  • Detection of At-Risk Students in Programming Courses

    Igawa I., Taniguchi Y., Minematsu T., Okubo F., Shimada A.

    30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings   1   308 - 313   2022.11   ISBN:9789869721493

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    Since the demand for programmers is increasing, programming courses are being offered widely. In this context, students' motivation can be damaged by difficulties they encounter in their programming courses. Although teachers' support is necessary to prevent such an issue, it is impossible for teachers to directly monitor all students' programming activities at the same time and determine which students have troubles with programming. Therefore, several studies have been conducted to help teachers monitor students. However, these studies do not provide an understanding of the activities of students who do not run their code, which may lead researchers to miss students who are in trouble. In this paper, we propose an indicator for detecting students who need coding support by analyzing programming logs that are recorded even when the students do not run their code. This gives teachers deeper insight into the students' programming performance. Although further work remains, the validation of this indicator shows that it could detect those students who are in trouble.

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  • Assessment of At-Risk Students' Predictions From e-Book Activities Representations in Practical Applications

    Erwin D. Lopez Z, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings   1   279 - 288   2022.11

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    The use of e-book reading systems such as Bookroll, and their ability to record readers' activities allows the design of predictive models capable of identifying at-risk students from their reading characteristics. Even though previous works have obtained promising results in this task, these results may not evidence the expected prediction performance in practical applications due to their selected assessment methods. Accordingly, in this paper, we assess this performance in two practical scenarios. The first is when we keep stored data from previous years of our course which can be used to train our model, and the second is when we only have data from a different course to use in this training process. In order to obtain a more accurate assessment, we collected 92, 574 samples of predictive performances from different models under the above-mentioned conditions. We also considered different feature representations along with variational latent representations, which can leverage our previous data to automatically design general hidden features. From our results, we understand that in the first condition we can expect a relatively good predictive performance, especially when using variational latent representations. However, in the second condition we found that even when using them, the predictive performances are very limited resulting in an impractical solution.

  • Topic-Based Representation of Learning Activities for New Learning Pattern Analytics

    Jinghao Wang, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings   1   268 - 278   2022.11

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    In recent years, several kinds of e-learning systems, such as e-book and Learning Management System (LMS) have been widely used in the field of education. When students access these systems, their activities on the systems will be continuously and automatically recorded and stored as learning logs. As the learning logs are stored in association with students and indicate students' learning activities, most studies have been “student-based” learning log analyses focused on students and each student's learning behavior. However, the “student-based” learning log analysis focuses on each student's learning behavior during the entire lesson (for example, studied well or didn't study enough) and cannot show what they learned. Therefore, if there is a need to investigate students' learning behavior regarding each topic of the lesson, such as which topic is learned well and which not in order to optimize the syllabus, we cannot conduct “student-based” learning log analysis directly. Instead of “student-based” learning log analyses, this study describes a method of “learning-topic-based” learning log analysis. We will show how to convert a learning log associated with students into a learning-topic-associated one and shape the logs into a two-dimensional matrix of learning topics and learning activities. Then we apply Non-negative Matrix Factorization (NMF) to the matrix in order to extract the learning patterns by activity. In addition, we make a three-dimensional matrix (tensor) of students, learning topics, and learning activities by subdividing the learning activities of each learning topic by students. We then apply Non-negative Tensor Factorization (NTF) to the tensor to extract detailed learning patterns. The methods proposed in this study will help teachers to have a comprehensively view of students' learning behaviors towards each learning topic easily even if the learning log is in a large-scale, so teachers can adjust syllabus according to the attracted learning behaviors, which is helpful to increase learning efficiency.

  • Detection of At-Risk Students in Programming Courses

    Ikkei Igawa, Yuta Taniguchi, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada

    30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings   1   308 - 313   2022.11

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    Since the demand for programmers is increasing, programming courses are being offered widely. In this context, students' motivation can be damaged by difficulties they encounter in their programming courses. Although teachers' support is necessary to prevent such an issue, it is impossible for teachers to directly monitor all students' programming activities at the same time and determine which students have troubles with programming. Therefore, several studies have been conducted to help teachers monitor students. However, these studies do not provide an understanding of the activities of students who do not run their code, which may lead researchers to miss students who are in trouble. In this paper, we propose an indicator for detecting students who need coding support by analyzing programming logs that are recorded even when the students do not run their code. This gives teachers deeper insight into the students' programming performance. Although further work remains, the validation of this indicator shows that it could detect those students who are in trouble.

  • Visualizing Source-Code Evolution for Understanding Class-Wide Programming Processes

    Yuta Taniguchi, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada

    Sustainability (Switzerland)   14 ( 13 )   2022.7   eISSN:2071-1050

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    The COVID-19 pandemic has led to an increase in online classes, and programming classes are no exception. In such a learning environment, understanding every student’s programming process is mostly impractical for teachers, despite its significance in supporting students. Giving teachers feedback on programming processes is a typical approach to the problem. However, few studies have focused on visual representations of the evolution process of source-code contents; it remains unclear what visual representation would be effective to this end and how teachers value such feedback. We propose two feedback tools for teachers. These tools visualize the temporal evolution of source-code contents at different granularities. An experiment was conducted in which several university teachers performed a user evaluation of the tools, particularly with regard to their usefulness for reviewing past programming classes taught by another teacher. Questionnaire results showed that these tools are helpful for understanding programming processes. The tools were also found to be complementary, with different aspects being highly evaluated. We successfully presented concrete visual representations of programming processes as well as their relative strengths and weaknesses for reviewing classes; this contribution may serve as a basis for future real-time use of these tools in class.

    DOI: 10.3390/su14138084

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  • Live Sharing of Learning Activities on E-Books for Enhanced Learning in Online Classes

    Yuta Taniguchi, Takuro Owatari, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada

    Sustainability   14 ( 12 )   6946 - 6946   2022.6   eISSN:2071-1050

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    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.

    DOI: 10.3390/su14126946

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  • Corrigendum: “On the computing powers of L-reductions of insertion languages” (Theoretical Computer Science (2021) 862 (224–235), (S030439752030668X), (10.1016/j.tcs.2020.11.029))

    Fumiya Okubo, Takashi Yokomori

    Theoretical Computer Science   920   113 - 113   2022.6   ISSN:0304-3975 eISSN:1879-2294

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    The authors regret that the proof for Theorem 4 in the article “On the computing powers of [Formula presented]-reductions of insertion languages” was incomplete, which was pointed out by Kaoru Fujioka, Fukuoka Women's University, Japan. The proof needs some supplementary corrections for constructing the insertion system γ and [Formula presented]. Specifically, in the proof (on page 231), 1. line 4: Correct as [Formula presented], we construct the following rules: [Formula presented] 3. after line 11: Insert the following: • If there exists a rule

    DOI: 10.1016/j.tcs.2021.06.028

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  • Exploring the use of probabilistic latent representations to encode the students' reading characteristics

    Erwin D. Lopez Z, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    Proceedings of the 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior   1 - 10   2022.3

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    Exploring the use of probabilistic latent representations to encode the students' reading characteristics

  • The 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior

    Brendan Flanagan, Atsushi Shimada, Fumiya Okubo, Huiyong Li, Rwitajit Majumdar, Hiroaki Ogata

    Companion Proceedings 12th International Conference on Learning Analytics & Knowledge (LAK22)   152 - 155   2022.3

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    The 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior

  • Predicting student performance based on Lecture Materials data using Neural Network Models

    Sukrit Leelaluk, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    Proceedings of the 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior   1 - 10   2022.3

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  • New Perspective on Input Feature Analysis for Early Feedback by Student Performance Prediction Considering the Future Effect

    Ryusuke Murata, Fumiya Okubo, Tsubasa Minematsu, Yuta Taniguchi, Atsushi Shimada

    Companion Proceedings 12th International Conference on Learning Analytics & Knowledge (LAK22)   95 - 97   2022.3

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    New Perspective on Input Feature Analysis for Early Feedback by Student Performance Prediction Considering the Future Effect

  • How Does Analysis of Handwritten Notes Provide Better Insights for Learning Behavior?

    Boyi Li, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    LAK22: 12th International Learning Analytics and Knowledge Conference   549 - 555   2022.3   ISBN:978-1-4503-9573-1

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    Handwritten notes are one important component of students' learning process, which is used to record what they have learned in class or tease out knowledge after class for reflection and further strengthen the learning effect. It also helps a lot during review. We hope to divide handwritten notes (Japanese) into different parts, such as text, mathematical expressions, charts, etc., and quantify them to evaluate the condition of the notes and compare them among students. At the same time, data on students' learning behaviors in the course are collected through the online education platform, such as the use time of textbook and attendance, as well as the scores of the online quiz and course grade. In this paper, the analysis of the relationship between the segmentation results of handwritten notes and learning behavior are reported, as well as the research on automatic page segmentation based on deep learning.

    DOI: 10.1145/3506860.3506915

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    Other Link: https://dl.acm.org/doi/pdf/10.1145/3506860.3506915

  • Coding Trajectory Map: Student Programming Situations Made Visually Locatable

    Yuta Taniguchi, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada

    Companion Proceedings 12th International Conference on Learning Analytics & Knowledge (LAK22)   98 - 100   2022.3

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    Coding Trajectory Map: Student Programming Situations Made Visually Locatable

  • Chemical Reaction Regular Grammars

    Fumiya Okubo, Kaoru Fujioka, Takashi Yokomori

    New Generation Computing   40 ( 2 )   659 - 680   2022.3   ISSN:0288-3635 eISSN:1882-7055

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    We propose a new type of computing devices based on grammatical formulation augmented by multiset storages, called chemical reaction regular grammars (CRRGs), and investigate some formal language theoretic characterizations of CRRGs including their generative capabilities. Shortly, a CRRG is a regular grammar with multisets, while its computational capability exhibits very intriguing aspects depending on the manners of rule applications. Firstly, we show that the class of languages (denoted by CRRLλ) generated by CRRGs coincides with the class of languages accepted by chemical reaction automata (Okubo and Yokomori in Nat Comput 15(2): 215–224, 2016), whose implication is that the computing power of CRRGs is also equivalent to that of several known devices introduced from different motivations such as Petri nets (Peterson in ACM Comput Surv 9(3):223–252, 1977) and partially blind 1-way multicounter machines (Greibach in Theor Comput Sci 7:311–324, 1979). Second, a new manner of rewriting strategy is integrated into CRRGs and we show that CRRGs working in maximal-sequential manner can generate any recursively enumerable language, which is an unexpected result with a surprise. In contrast, it is also shown that regulated controls due to regular sets and matrix constraints do not enhance the computing power of CRRGs. Third, for each k≥ 1 a subclass of languages k-CRRLλ is considered, where k is the number of different symbols for multisets of CRRGs. We show that the class of languages is a full principal semi-AFL, which is obtained from a characterization result that L is in k-CRRLλ iff L= h(g- 1(Bk) ∩ R) for some homomorphisms g, h, a regular set R, where Bk is a paritally balanced language over k-symbol alphabet.

    DOI: 10.1007/s00354-022-00160-8

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    Other Link: https://link.springer.com/article/10.1007/s00354-022-00160-8/fulltext.html

  • $$mathcal {L}$$-reduction computation revisited

    Kaoru Fujioka, Fumiya Okubo, Takashi Yokomori

    Acta Informatica   59 ( 4 )   409 - 426   2022.3   ISSN:0001-5903 eISSN:1432-0525

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    Let K and L be two languages over Σ and Γ (with Γ ⊂ Σ), respectively. Then, the L-reduction of K, denoted by K%L, is defined by {u0u1⋯un∈(Σ-Γ)∗∣u0v1u1⋯vnun∈K,vi∈L(1≤i≤n)}. This is extended to language classes as follows: K%L={K%L∣K∈K,L∈L}. In this paper, we investigate the computing powers of K%L in which K ranges among various classes of INSji and min-LIN, while L is taken as DYCK and F, where INSji: the class of insertion languages of weight (j, i), min-LIN: the class of minimal linear languages, DYCK: the class of Dyck languages, and F: the class of finite languages. The obtained results include:INS11%DYCK=REINSi0%F=INSj1%F=CF (for i≥ 3 and j≥ 1)INS20%DYCK=INS20min-LIN%F1=LIN where RE, CF, LIN, F1 are classes of recursively enumerable, of context-free, of linear languages, and of singleton languages over unary alphabet, respectively. Further, we provide a very simple alternative proof for the known result min-LIN%DYCK2=RE. We also show that with a certain condition, for the class of context-sensitive languages CS, there exists no K such that K%DYCK=CS, which is in marked contrast to the characterization results mentioned above for other classes in Chomsky hierarchy. It should be remarked from the viewpoint of molecular computing theory that the notion of L-reduction is naturally motivated by a molecular biological functioning well-known as RNA splicing occurring in most eukaryotic genes.

    DOI: 10.1007/s00236-022-00418-0

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    Other Link: https://link.springer.com/article/10.1007/s00236-022-00418-0/fulltext.html

  • The 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior

    Brendan Flanagan, Atsushi Shimada, Fumiya Okubo, Huiyong Li, Rwitajit Majumdar, Hiroaki Ogata

    Companion Proceedings 12th International Conference on Learning Analytics & Knowledge (LAK22)   152 - 155   2022.3

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  • Predicting student performance based on Lecture Materials data using Neural Network Models

    Sukrit Leelaluk, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    Proceedings of the 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior   1 - 10   2022.3

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  • New Perspective on Input Feature Analysis for Early Feedback by Student Performance Prediction Considering the Future Effect

    Ryusuke Murata, Fumiya Okubo, Tsubasa Minematsu, Yuta Taniguchi, Atsushi Shimada

    Companion Proceedings 12th International Conference on Learning Analytics & Knowledge (LAK22)   95 - 97   2022.3

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  • Exploring the use of probabilistic latent representations to encode the students' reading characteristics

    Erwin D. Lopez Z, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    Proceedings of the 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior   1 - 10   2022.3

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  • Coding Trajectory Map: Student Programming Situations Made Visually Locatable

    Yuta Taniguchi, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada

    Companion Proceedings 12th International Conference on Learning Analytics & Knowledge (LAK22)   98 - 100   2022.3

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  • Topic-Based Representation of Learning Activities for New Learning Pattern Analytics

    Wang, JH; Minematsu, T; Taniguchi, Y; Okubo, F; Shimad, A

    30TH INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION, ICCE 2022, VOL 1   268 - 278   2022   ISBN:978-986-972-149-3

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  • Scaled-Dot Product Attention for Early Detection of At-risk Students

    Leelaluk S., Minematsu T., Taniguchi Y., Okubo F., Yamashita T., Shimada A.

    Proceedings - 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022   316 - 322   2022   ISBN:9781665491174

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    Students' performance prediction is essential for instructors to observe each student's learning behavior to discover which students have become at-risk. Early prediction helps instructors to intervene in time and provide academic support to these students. However, instructors should grasp essential behavior points to survey students' academic performance. In this study, we propose the Scaled-Dot Product Attention that can mine the relationship between the student's learning behaviors and performance to find the essential features that directly affect students' performance. In this study, we tested the early prediction performance of Scaled-Dot Product Attention with conventional algorithms. We then investigated essential lectures or features related to students' learning activities. From the result, we found that Scaled-Dot Product Attention outperformed the conventional algorithms to identify at-risk students and found the important lectures and students' actions.

    DOI: 10.1109/TALE54877.2022.00059

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  • Predicting student performance based on Lecture Materials data using Neural Network Models

    Leelaluk S., Minematsu T., Taniguchi Y., Okubo F., Shimada A.

    CEUR Workshop Proceedings   3120   11 - 20   2022   ISSN:16130073

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    Student Performance Prediction is essential for learning analysis of the students' learning behavior to discovering at-risk students for the early invention to support students. This study transforms the students' reading behavior into a two-dimensional matrix input based on each lecture material's reading behavior. The matrix input will be updated by accumulating the value for each week for performance prediction week by week. The multilayer perceptron neural network is employed to receive the matrix input and give feedback as a student's criteria consist of at-risk or no-risk students. This study considers the accuracy of a model considering between on contents information and weekly information. We also investigate the switching of learning materials' order, the feature importance of the reading operation on an event stream, and the difference in reading behavior between at-risk and no-risk students. These can help the instructors for an early invention to support at-risk students.

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  • Exploring the use of probabilistic latent representations to encode the students' reading characteristics

    Lopez E.D., Minematsu T., Yuta T., Okubo F., Shimada A.

    CEUR Workshop Proceedings   3120   1 - 10   2022   ISSN:16130073

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    The emergence of digital textbook reading systems such as Bookroll, and their ability of recording reader interactions has opened the possibility of analyzing the students reading behaviors and characteristics. To date, several works have conducted compelling analyses characterizing the different types of students with the use of clustering ML models, while others have used supervised ML models to predict their academic performance. The main characteristic these models share is that internally they simplify the students' data into a latent representation to get an insight or make a prediction. Nevertheless, these representations are oversimplified, otherwise difficult to interpret. Accordingly, the present work explores the use of Variational Autoencoders to make more interpretable and complex latent representations. After a brief description of these models, we present and discuss the results of four explorative studies when using the LAK22 Data Challenge Workshop datasets. Our results show that the probabilistic latent representations generated by the proposed models preserve the student reading characteristics, allowing a better visual interpretation when using 3 dimensions. Also, they allow supervised regressive and classification models to have a more stable and less overfitted learning process, which also allows some of them to make better score predictions.

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  • Assessment of At-Risk Students' Predictions From e-Book Activities Representations in Practical Applications

    Lopez, EDZ; Minematsu, T; Taniguchi, Y; Okubo, F; Shimada, A

    30TH INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION, ICCE 2022, VOL 1   279 - 288   2022   ISBN:978-986-972-149-3

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  • Background Subtraction Network Module Ensemble for Background Scene Adaptation

    Hamada, T; Minematsu, T; Simada, A; Okubo, F; Taniguchi, Y

    2022 18TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE (AVSS 2022)   2022   ISBN:978-1-6654-6382-9

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    Publisher:AVSS 2022 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance  

    Background subtraction networks outperform traditional hand-craft background subtraction methods. The main advantage of background subtraction networks is their ability to automatically learn background features for training scenes. When applying the trained network to new target scenes, adapting the network to the new scenes is crucial. However, few studies have focused on reusing multiple trained models for new target scenes. Considering background changes have several categories, such as illumination changes, a model trained for each background scene can work effectively for the target scene similar to the training scene. In this study, we propose a method to ensemble the module networks trained for each background scene. Experimental results show that the proposed method is significantly more accurate compared with the conventional methods in the target scene by tuning with only a few frames.

    DOI: 10.1109/AVSS56176.2022.9959316

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  • Detection of At-Risk Students in Programming Courses

    Igawa, I; Taniguchi, Y; Minematsu, T; Okubo, F; Shimada, A

    30TH INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION, ICCE 2022, VOL 1   308 - 313   2022   ISBN:978-986-972-149-3

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  • Theory of reaction automata: a survey

    Takashi Yokomori, Fumiya Okubo

    Journal of Membrane Computing   2021.3

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    DOI: 10.1007/s41965-021-00070-6

  • On the computing powers of L-reductions of insertion languages

    Fumiya Okubo, Takashi Yokomori

    Theoretical Computer Science   2021.3

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    © 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.

    DOI: 10.1016/j.tcs.2020.11.029

  • Exploring Factors that Influence Collaborative Problem Solving Awareness in Science Education

    Li Chen, Koichi Inoue, Yoshiko Goda, Fumiya Okubo, Yuta Taniguchi, Misato Oi, Shin’ichi Konomi, Hiroaki Ogata, Masanori Yamada

    Technology, Knowledge and Learning   25 ( 2 )   337 - 366   2020.6

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    © 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.

    DOI: 10.1007/s10758-020-09436-8

  • Direction of collaborative problem solving-based STEM learning by learning analytics approach

    Li Chen, Nobuyuki Yoshimatsu, Yoshiko Goda, Fumiya Okubo, Yuta Taniguchi, Misato Oi, Shin’ichi Konomi, Atsushi Shimada, Hiroaki Ogata, Masanori Yamada

    Research and Practice in Technology Enhanced Learning   14 ( 1 )   2019.12

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    © 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.

    DOI: 10.1186/s41039-019-0119-y

  • E-book learner behaviors difference under two meaningful learning support environments

    Jingyun Wang, Atsushi Shimada, Fumiya Okubo

    ICCE 2019 - 27th International Conference on Computers in Education, Proceedings   1   342 - 347   2019.11

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    © 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.

  • Decomposition and factorization of chemical reaction transducers

    Fumiya Okubo, Takashi Yokomori

    Theoretical Computer Science   777   431 - 442   2019.7

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    © 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.

    DOI: 10.1016/j.tcs.2019.01.032

  • Integrating Multimodal Learning Analytics and Inclusive Learning Support Systems for People of All Ages

    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)   11577 LNCS   469 - 481   2019.6

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    © 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.

    DOI: 10.1007/978-3-030-22580-3_35

  • Exploring the Relationships between Reading Behavior Patterns and Learning Outcomes Based on Log Data from E-Books: A Human Factor Approach

    Chengjiu Yin, Masanori Yamada, Misato Oi, Atsushi Shimada, Fumiya Okubo, Kentaro Kojima, Hiroaki Ogata

    International Journal of Human-Computer Interaction   35 ( 4-5 )   313 - 322   2019.3

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    © 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.

    DOI: 10.1080/10447318.2018.1543077

  • Relationships between Collaborative Problem Solving, Learning Performance and Learning Behavior in Science Education

    Li Chen, Hirokazu Uemura, Hao Hao, Yoshiko Goda, Fumiya Okubo, Yuta Taniguchi, Misato Oi, Shin'ichi Konomi, Hiroaki Ogata, Masanori Yamada

    Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018   17 - 24   2019.1

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    © 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.

    DOI: 10.1109/TALE.2018.8615254

  • Computing with multisets: A survey on reaction automata theory

    Takashi Yokomori, Fumiya Okubo

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   10936 LNCS   421 - 431   2018.8

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    © 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.

    DOI: 10.1007/978-3-319-94418-0_42

  • Towards supporting multigenerational co-creation and social activities: Extending learning analytics platforms and beyond

    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)   10922 LNCS   82 - 91   2018.7

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    © 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.

    DOI: 10.1007/978-3-319-91131-1_6

  • Automatic Summarization of Lecture Slides for Enhanced Student Preview-Technical Report and User Study

    Atsushi Shimada, Fumiya Okubo, Chengjiu Yin, Hiroaki Ogata

    IEEE Transactions on Learning Technologies   11 ( 2 )   165 - 178   2018.4

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    © 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.

    DOI: 10.1109/TLT.2017.2682086

  • Online change detection for monitoring individual student behavior via clickstream data on E-book system

    Atsushi Shimada, Yuta Taniguchi, Fumiya Okubo, Shin’ichi Konomi, Hiroaki Ogata

    ACM International Conference Proceeding Series   446 - 450   2018.3

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    © 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.

    DOI: 10.1145/3170358.3170412

  • On the prediction of students’ quiz score by recurrent neural network

    Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada, Yuta Taniguchi, Konomi Shin’ichi

    CEUR Workshop Proceedings   2163   2018.3

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    © 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.

  • Learning analytics of the relationships among self-regulated learning, learning behaviors, and learning performance

    Masanori Yamada, Atsushi Shimada, Fumiya Okubo, Misato Oi, Kentaro Kojima, Hiroaki Ogata

    Research and Practice in Technology Enhanced Learning   12 ( 1 )   2017.12

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    © 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.

    DOI: 10.1186/s41039-017-0053-9

  • Students' performance prediction using data of multiple courses by recurrent neural network

    Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada, Shin'ichi Konomi

    Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings   439 - 444   2017.12

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    © 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.

  • Effects of prior knowledge of high achievers on use of e-book highlights and annotations

    Misato Oi, Fumiya Okubo, Yuta Taniguchi, Masanori Yamada, Shin'ichi Konomi

    Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings   682 - 687   2017.12

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    © 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.

  • Analysis on students' usage of highlighters on e-textbooks in classroom

    Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada, Shin'ichi Konomi

    Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings   514 - 516   2017.12

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    © 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.

  • Exploring students' learning journals with web-based interactive report tool

    Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada, Shin'Ichi Konomi

    14th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2017   251 - 254   2017.10

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    © 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.

  • A visualization system for predicting learning activities using state transition graphs

    Fumiya Okubo, Atsushi Shimada, Yuta Taniguchi, Shin'Ichi Konomi

    14th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2017   173 - 180   2017.10

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    © 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.

  • Morphic characterizations of language families based on local and star languages

    Fumiya Okubo, Takashi Yokomori

    Fundamenta Informaticae   154 ( 1-4 )   323 - 341   2017.8

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    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.

    DOI: 10.3233/FI-2017-1569

  • Learning analytics for E-book-based educational big data in higher education

    Hiroaki Ogata, Misato Oi, Kousuke Mohri, Fumiya Okubo, Atsushi Shimada, Masanori Yamada, Jingyun Wang, Sachio Hirokawa

    Smart Sensors at the IoT Frontier   327 - 350   2017.5

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    DOI: 10.1007/978-3-319-55345-0_13

  • Reproducibility of Findings from Educational Big Data: A Preliminary Study Reviewed International journal

    Misato Oi, Yamada, M., Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata

    Proceedings of the 7th International Learning Analytics & Knowledge Conference (LAK2017)   536 - 537   2017.3

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  • Finding traces of high and low achievers by analyzing undergraduates' e-book logs

    Misato Oi, Masanori Yamada, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata

    CEUR Workshop Proceedings   1828   15 - 22   2017.3

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    © 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.

  • M2B System: A Digital Learning Platform for Traditional Classrooms in University Reviewed International journal

    Hiroaki Ogata, Yuta Taniguchi, Daiki Suehiro, Atsushi Shimada, Misato Oi, Fumiya Okubo, Yamada, M., Kentaro Kojima

    Practitioner Track Proceedings of the 7th International Learning Analytics & Knowledge Conference (LAK2017)   154 - 161   2017.3

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  • A Neural Network Approach for Students’ Performance Prediction Reviewed International journal

    Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada, Hiroaki Ogata

    Proceedings of the 7th International Learning Analytics & Knowledge Conference (LAK2017)   598 - 599   2017.3

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  • Learning Analytics in Ubiquitous Learning Environments: Self-Regulated Learning Perspective Reviewed International journal

    Yamada, M., Misato Oi, Fumiya Okubo, Atsushi Shimada, Kentaro Kojima, Hiroaki Ogata

    Proceedings of the 24th International Conference on Computers in Education (ICCE2016)   306 - 314   2016.12

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  • Browsing-Pattern Mining from e-Book Logs with Non-negative Matrix Factorization Reviewed International journal

    Atsushi Shimada, Fumiya Okubo, Hiroaki Ogata

    Proceedings of the 9th International Conference on Educational Data Mining   636 - 637   2016.7

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  • Bayesian Network for Predicting Students’ Final Grade using e-book Logs in University Education Reviewed International journal

    Kousuke Mouri, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata

    Proceedings of the 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016)   85 - 89   2016.7

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  • 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 Invited

    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

    教育システム情報学会誌   33 ( 2 )   58 - 66   2016.5

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  • Profiling High-achieving Students using E-book-based Logs Reviewed International journal

    Kousuke Mouri, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata

    Proceedings of the 1st International Workshop on Learning Analytics Across Physical and Digital Spaces (Cross-LAK 2016)   5 - 9   2016.4

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  • Learning Activity Features of High Performance Students Reviewed International journal

    Fumiya Okubo, Sachio Hirokawa, Misato Oi, Atsushi Shimada, Kojima Kentaro, Yamada Masanori, Hiroaki Ogata

    Proceedings of the 1st International Workshop on Learning Analytics Across Physical and Digital Spaces (Cross-LAK 2016)   28 - 33   2016.4

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  • Automatic Generation of Personalized Review Materials Based on Across-Learning-System Analysis Reviewed International journal

    Atsushi Shimada, Fumiya Okubo, Chengjiu Yin, Hiroaki Ogata

    Proceedings of the 1st International Workshop on Learning Analytics Across Physical and Digital Spaces (Cross-LAK 2016)   22 - 27   2016.4

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  • 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 Invited Reviewed International journal

    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

    基幹教育紀要   2   61 - 72   2016.3

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  • 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 Invited Reviewed International journal

    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

    基幹教育紀要   2   48 - 60   2016.3

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  • Analysis of Links among E-books in Undergraduates’ E-Book Logs

    Misato Oi, CHENGJIU YIN, Fumiya Okubo, Atsushi Shimada, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata

    Workshop Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015)   2015.11

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  • Identifying and Analyzing the Learning Behaviors of Students using e‐Books

    CHENGJIU YIN, Fumiya Okubo, Atsushi Shimada, Sachio Hirokawa, Misato Oi, Hiroaki Ogata

    Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015)   2015.11

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  • Analysis of Preview and Review Patterns in Undergraduates’ E‐Book Logs

    Misato Oi, Fumiya Okubo, Atsushi Shimada, CHENGJIU YIN, Hiroaki Ogata

    Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015)   2015.11

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  • Automatic Summarization of Lecture Slides for Enhanced Student Preview

    Atsushi Shimada, Fumiya Okubo, CHENGJIU YIN, Hiroaki Ogata

    Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015)   2015.11

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  • E‐Book‐based Learning Analytics in University Education

    Hiroaki Ogata, CHENGJIU YIN, Misato Oi, Fumiya Okubo, Atsushi Shimada, Kojima Kentaro, Masanori Yamada

    Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015)   2015.11

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  • Visualization and Prediction of Learning Activities by Using Discrete Graphs

    Fumiya Okubo, Atsushi Shimada, CHENGJIU YIN, Hiroaki Ogata

    Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015)   2015.11

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  • Analysis of Preview Behavior in E-Book System

    Atsushi Shimada, Fumiya Okubo, CHENGJIU YIN, Misato Oi, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata

    Workshop Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015)   2015.11

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    Language:English  

  • Analyzing the Features of Learning Behaviors of Students using e-Books

    CHENGJIU YIN, Fumiya Okubo, Atsushi Shimada, Misato Oi, Sachio Hirokawa, Masanori Yamada, Kojima Kentaro, Hiroaki Ogata

    Workshop Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015)   2015.11

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    Language:English  

  • Informal Learning Behavior Analysis Using Action Logs and Slide Features in E-textbooks

    Atsushi Shimada, Fumiya Okubo, CHENGJIU YIN, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata

    Proceedings of the 15th IEEE International Conference on Advanced Learning Technologies (ICALT 2015)   116 - 117   2015.7

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    Language:English  

  • Preliminary Research on Self-regulated Learning and Learning Logs in a Ubiquitous Learning Environment

    Masanori Yamada, CHENGJIU YIN, Atsushi Shimada, Kojima Kentaro, Fumiya Okubo, Hiroaki Ogata

    Proceedings of the 15th IEEE International Conference on Advanced Learning Technologies (ICALT 2015)   2015.7

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    Language:English  

  • Finite Automata with Multiset Memory: A New Characterization of Chomsky Hierarchy Reviewed International journal

    Fumiya Okubo, Takashi Yokomori

    Fundamenta Informaticae   ( 138 )   31 - 44   2015.4

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

  • The Computational Capability of Chemical Reaction Automata Reviewed International journal

    Fumiya Okubo, Takashi Yokomori

    Lecture Notes in Computer Science   ( 8727 )   53 - 66   2014.9

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    Language:English   Publishing type:Research paper (international conference proceedings)  

  • REACTION AUTOMATA WORKING IN SEQUENTIAL MANNER Reviewed International journal

    Fumiya Okubo

    RAIRO-THEORETICAL INFORMATICS AND APPLICATIONS   48 ( 1 )   23 - 38   2014.1

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

    DOI: 10.1051/ita/2013047

  • On language classes defined by reaction automata

    Fumiya Okubo

    早稲田大学教育学部 学術研究 自然科学編   61   39 - 46   2013.3

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    Language:English   Publishing type:Research paper (bulletin of university, research institution)  

  • On the properties of language classes defined by bounded reaction automata Reviewed International journal

    Fumiya Okubo, Satoshi Kobayashi, Takashi Yokomori

    THEORETICAL COMPUTER SCIENCE   454   206 - 221   2012.10

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

    DOI: 10.1016/j.tcs.2012.03.024

  • Automata inspired by biochemical reaction

    Fumiya Okubo, Satoshi Kobayashi, Takashi Yokomori

    京都大学数理解析研究所講究録   1799   179 - 182   2012.6

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Books

  • Learning Support Systems Based on Cohesive Learning Analytics, in "Emerging Trends in Learning Analytics: Leveraging the Power of Education Data" (ed. Myint Swe Khine)

    Fumiya Okubo, Masanori Yamada, Misato Oi, Atsushi Shimada, Yuta Taniguchi, Shin’ichi Konomi(Role:Joint author)

    Brill Publisher  2019.5 

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    Responsible for pages:pp.223-248   Language:English   Book type:Scholarly book

  • The Computing Power of Determinism and Reversibility in Chemical Reaction Automata, in "Reversibility and Universality" (ed. Andrew Adamatzky)

    Fumiya Okubo, Takashi Yokomori(Role:Joint author)

    Springer International Publishing  2018.2 

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    Responsible for pages:Emergence, Complexity and Computation, vol.30, pp.279-298   Language:English   Book type:Scholarly book

  • Learning Analytics for E-Book-Based Educational Big Data in Higher Education, in "Smart Sensors at the IoT Frontier" (eds. Hiroto Yasuura, Chong-Min Kyung, Yongpan Liu, Youn-Long Lin),

    Hiroaki Ogata, Misato Oi, Kousuke Mohri, Fumiya Okubo, Atsushi Shimada, Masanori Yamada, Jingyun Wang, Sachio Hirokawa(Role:Joint author)

    Springer  2017.5 

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    Responsible for pages:pp.327-350   Language:English   Book type:Scholarly book

  • Recent Developments on Reaction Automata Theory: A Survey, Recent Advances in Natural Computing (eds. Yasuhiro Suzuki, Masami Hagiya), series in Mathematics for Industry

    Fumiya Okubo, Takashi Yokomori(Role:Joint author)

    2014.7 

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    Responsible for pages:vol.9, pp.1-22   Language:English  

Presentations

  • Automata inspired by biochemical reaction

    大久保 文哉, 横森 貴, 小林 聡

    LAシンポジウム  2012.1 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Venue:京都大学   Country:Japan  

  • On the Computational Power of Reaction Automata Working in Sequential Manner

    Fumiya Okubo

    The 4th Workshop on Non-Classical Models for Automata and Applications (NCMA 2012)  2012.8 

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    Language:English   Presentation type:Oral presentation (general)  

    Venue:Fribourg   Country:Switzerland  

  • Recent Developments of Reaction Automata International conference

    Fumiya Okubo, Takashi Yokomori

    The 7th International Workshop on Natural Computing (IWNC7)  2013.3 

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    Language:English   Presentation type:Oral presentation (general)  

    Venue:Tokyo   Country:Japan  

  • The Computational Capability of Chemical Reaction Automata International conference

    大久保 文哉, 横森 貴

    20th International Conference on DNA Computing and Molecular Programming  2014.9 

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    Language:English   Presentation type:Oral presentation (general)  

    Venue:Kyoto   Country:Japan  

  • Visualization and Prediction of Learning Activities by Using Discrete Graphs International conference

    Fumiya Okubo, Atsushi Shimada, CHENGJIU YIN, Hiroaki Ogata

    The 23rd International Conference on Computers in Education (ICCE 2015)  2015.12 

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    Language:English  

    Country:China  

  • Bayesian Network for Predicting Students’ Final Grade using e-book Logs in University Education International conference

    Kousuke Mouri, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata

    The 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016)  2016.7 

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    Event date: 2016.5

    Language:English  

    Country:United Kingdom  

  • Learning Activity Features of High Performance Students International conference

    Fumiya Okubo, Sachio Hirokawa, Misato Oi, Atsushi Shimada, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata

    Cross-LAK 2016: International Workshop on Learning Analytics Across Physical and Digital Spaces  2016.4 

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    Event date: 2016.5

    Language:English  

    Country:United Kingdom  

  • Automatic Generation of Personalized Review Materials Based on Across-Learning-System Analysis International conference

    Atsushi Shimada, Fumiya Okubo, Chengjiu Yin, Hiroaki Ogata

    Cross-LAK 2016: International Workshop on Learning Analytics Across Physical and Digital Spaces  2016.4 

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    Event date: 2016.5

    Language:English  

    Country:United Kingdom  

  • Profiling High-achieving Students using E-book-based Logs International conference

    Kousuke Mouri, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata

    Cross-LAK 2016: International Workshop on Learning Analytics Across Physical and Digital Spaces  2016.4 

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    Event date: 2016.5

    Language:English  

    Country:United Kingdom  

  • Browsing-Pattern Mining from e-Book Logs with Non-negative Matrix Factorization International conference

    Atsushi Shimada, Fumiya Okubo, Hiroaki Ogata

    The 9th International Conference on Educational Data Mining  2016.7 

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    Event date: 2016.5

    Language:English  

    Country:United Kingdom  

  • A Computing Model for Biochemical Reactions

    大久保 文哉

    CBI学会2013年大会 

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    Language:Japanese  

    Venue:東京   Country:Japan  

  • On the Descriptional Complexity of Semi-Conditional Grammars and Simple Semi-Conditional Grammars

    大久保 文哉

    LAシンポジウム  2009.7 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Venue:仙台   Country:Japan  

  • Semi-conditional grammarとその記述的最適化問題について

    大久保 文哉

    早稲田大学教育学部数学科 第93回7階セミナー  2009.11 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Venue:早稲田大学   Country:Japan  

  • 形式言語理論によるDNAの分子生物学的な現象のモデル化について

    大久保 文哉

    Workshop 2010 on Mathematics and Mathematical Education  2010.9 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Venue:奈良教育大学   Country:Japan  

  • Smart Phone based Data Collecting System for Analyzing Learning Behaviors International conference

    CHENGJIU YIN, Fumiya Okubo, Atsushi Shimada, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata, Naomi Fujimura

    ICCE 2014  2014.11 

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    Language:English  

    Country:Japan  

  • Preliminary Research on Self-regulated Learning and Learning Logs in a Ubiquitous Learning Environment International conference

    Masanori Yamada, CHENGJIU YIN, Atsushi Shimada, Kojima Kentaro, Fumiya Okubo, Hiroaki Ogata

    The 15th IEEE International Conference on Advanced Learning Technologies (ICALT 2015)  2015.7 

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    Language:English  

    Country:Taiwan, Province of China  

  • Informal Learning Behavior Analysis Using Action Logs and Slide Features in E-textbooks International conference

    Atsushi Shimada, Fumiya Okubo, CHENGJIU YIN, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata

    The 15th IEEE International Conference on Advanced Learning Technologies (ICALT 2015)  2015.7 

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    Language:English  

    Country:Taiwan, Province of China  

  • 授業外学習支援のためのデジタル教材の自動要約

    島田 敬士, 大久保 文哉, 殷 成久, 緒方 広明

    信学技報 パターン認識・メディア理解(PRMU2015-81)  2015.9 

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    Language:Japanese  

    Country:Japan  

  • Analysis of Preview Behavior in E-Book System International conference

    Atsushi Shimada, Fumiya Okubo, CHENGJIU YIN, Misato Oi, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata

    The 1st workshop on e-Book-based Educational Big Data for Enhancing Teaching and Learning, in ICCE 2015  2015.11 

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    Language:English  

    Country:China  

  • Analyzing the Features of Learning Behaviors of Students using e-Books International conference

    CHENGJIU YIN, Fumiya Okubo, Atsushi Shimada, Misato Oi, Sachio Hirokawa, Masanori Yamada, Kojima Kentaro, Hiroaki Ogata

    The 1st workshop on e-Book-based Educational Big Data for Enhancing Teaching and Learning, in ICCE 2015  2015.11 

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    Language:English  

    Country:China  

  • Analysis of Links among E-books in Undergraduates’ E-Book Logs International conference

    Misato Oi, CHENGJIU YIN, Fumiya Okubo, Atsushi Shimada, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata

    The 1st workshop on e-Book-based Educational Big Data for Enhancing Teaching and Learning, in ICCE 2015  2015.11 

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    Language:English  

    Country:China  

  • Identifying and Analyzing the Learning Behaviors of Students using e‐Books International conference

    CHENGJIU YIN, Fumiya Okubo, Atsushi Shimada, Sachio Hirokawa, Hiroaki Ogata, Misato Oi

    The 23rd International Conference on Computers in Education (ICCE 2015)  2015.12 

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    Language:English  

    Country:China  

  • Analysis of Preview and Review Patterns in Undergraduates’ E‐Book Logs International conference

    Misato Oi, Fumiya Okubo, Atsushi Shimada, CHENGJIU YIN, Hiroaki Ogata

    The 23rd International Conference on Computers in Education (ICCE 2015)  2015.12 

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    Language:English  

    Country:China  

  • Analysis of Preview and Review Patterns in Undergraduates’ E‐Book Logs International conference

    Misato Oi, Fumiya Okubo, Atsushi Shimada, CHENGJIU YIN, Hiroaki Ogata

    The 23rd International Conference on Computers in Education (ICCE 2015)  2015.12 

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    Language:English  

    Country:China  

  • Automatic Summarization of Lecture Slides for Enhanced Student Preview International conference

    Atsushi Shimada, Fumiya Okubo, CHENGJIU YIN, Hiroaki Ogata

    The 23rd International Conference on Computers in Education (ICCE 2015)  2015.12 

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    Language:English  

    Country:China  

  • E‐Book‐based Learning Analytics in University International conference

    Hiroaki Ogata, CHENGJIU YIN, Misato Oi, Fumiya Okubo, Atsushi Shimada, Kojima Kentaro, Masanori Yamada

    The 23rd International Conference on Computers in Education (ICCE 2015)  2015.12 

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    Language:English  

    Country:China  

  • E-bookとLMSのログを用いた学習活動の分析と可視化

    大久保 文哉

    日本教育工学会SIG「教育・学習支援システムの開発と実践」(SIG-SYS)第4回研究会  2016.3 

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    Language:Japanese  

    Country:Japan  

  • 九州大学基幹教育におけるラーニングアナリティクスの研究と実践

    島田 敬士, 大久保 文哉, 殷 成久, 大井 京, 小島 健太郎, 山田 政寛, 緒方 広明

    電子情報通信学会総合大会  2016.3 

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    Language:Japanese  

    Country:Japan  

  • 大学におけるラーニングアナリティクスに基づく授業改善と教育革新

    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

    電子情報通信学会総合大会  2016.3 

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    Language:Japanese  

    Country:Japan  

  • ページ重要度に基づくデジタル教材の自動要約

    島田 敬士, 大久保 文哉, 緒方 広明

    第19回画像の認識・理解シンポジウム(MIRU2016)  2016.8 

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    Language:Japanese  

    Country:Japan  

  • 教育データのオープン化に向けて

    末廣 大貴, 毛利 考佑, 谷口 雄太, 大久保 文哉, 島田 敬士, 緒方 広明

    電子情報通信学会 パターン認識・メディア理解研究会 (PRMU2016)  2016.10 

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    Language:Japanese  

    Country:Japan  

  • Learning Analytics in Ubiquitous Learning Environments: Self-Regulated Learning Perspective International conference

    Masanori Yamada, Fumiya Okubo, Misato Oi, Atsushi Shimada, Kojima Kentaro, Hiroaki Ogata

    The 24th International Conference on Computers in Education (ICCE2016)  2016.11 

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    Language:English  

    Country:India  

  • Finding traces of high and low achievers by analyzing undergraduates’ e-book logs International conference

    Misato Oi, Masanori Yamada, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata

    Cross-LAK 2017: International Workshop on Learning Analytics Across Physical and Digital Spaces  2017.3 

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    Language:English  

    Country:Canada  

  • M2B System: A Digital Learning Platform for Traditional Classrooms in University International conference

    Hiroaki Ogata, Yuta Taniguchi, Daiki Suehiro, Atsushi Shimada, Misato Oi, Fumiya Okubo, Masanori Yamada, Kojima Kentaro

    The 7th International Learning Analytics & Knowledge Conference (LAK2017)  2017.3 

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    Language:English  

    Country:Canada  

  • A Neural Network Approach for Students’ Performance Prediction International conference

    Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada, Hiroaki Ogata

    The 7th International Learning Analytics & Knowledge Conference (LAK2017)  2017.3 

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    Language:English  

    Country:Canada  

  • Reproducibility of Findings from Educational Big Data: A Preliminary Study International conference

    Misato Oi, Masanori Yamada, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata

    The 7th International Learning Analytics & Knowledge Conference (LAK2017)  2017.3 

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    Language:English  

    Country:Canada  

  • 圃場環境ダイジェストシステムの開発とその評価

    志賀 寛羽, 谷口 雄太, 峰松 翼, 大久保 文哉, 島田 敬士, 谷口 倫一郎

    第34回教育学習支援情報システム研究発表会 (CLE34)  2021.5 

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    Language:Japanese  

    Country:Japan  

  • 学習記事推薦のための推薦システムの開発と手法の評価

    岡井 成遊, 大久保 文哉, 内山 英昭, 峰松 翼, 谷口 雄太, 島田 敬士

    第34回教育学習支援情報システム研究発表会 (CLE34)  2021.5 

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    Language:Japanese  

    Country:Japan  

  • 手書きノートのページセグメンテーションによる学習活動の分析

    李 柏毅, 峰松 翼, 谷口 雄太, 大久保 文哉, 島田 敬士

    第34回教育学習支援情報システム研究発表会 (CLE34)  2021.5 

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    Language:Japanese  

    Country:Japan  

  • 背景差分ニューラルネットワークの照明変動に関わるニューロンの特定と評価

    濵田 泰輝, 峰松 翼, 島田 敬士, 谷口 雄太, 大久保 文哉

    画像の認識・理解シンポジウム MIRU2021  2021.7 

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    Language:Japanese  

    Country:Japan  

  • 学生のコーディング過程理解のための教師支援

    谷口 雄太, 峰松 翼, 大久保 文哉, 島田 敬士

    第35回教育学習支援情報システム研究会(CLE35)  2021.12 

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    Language:Japanese  

    Country:Japan  

  • プログラミングログ分析による支援の必要な学生の検知指標の提案とフィードバックツールの開発

    井川 一渓, 谷口 雄太, 峰松 翼, 大久保 文哉, 島田 敬士

    第35回教育学習支援情報システム研究会(CLE35)  2021.12 

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    Language:Japanese  

    Country:Japan  

  • 科目の関連性情報を付加したカリキュラム情報閲覧システムの開発

    山本 雄介, 峰松 翼, 長沼 祥太郎, 谷口 雄太, 大久保 文哉, 島田 敬士

    第35回教育学習支援情報システム研究会(CLE35)  2021.12 

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    Language:Japanese  

    Country:Japan  

  • 学習記事共有ネットワークシステムの提案

    岡井 成遊, 峰松 翼, 大久保 文哉, 谷口 雄太, 内山 英昭, 島田 敬士

    第35回教育学習支援情報システム研究会(CLE35)  2021.12 

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    Country:Japan  

  • 学習テーマとその関連テーマによるデジタル教材のダイジェスト資料生成

    玉城亮治, 峰松翼, 谷口雄太, 大久保文哉, 島田敬士

    第37回教育学習支援情報システム研究会(CLE37)  2022.6 

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    Country:Japan  

  • プログラミング過程に着目した学生表現の学習

    谷口雄太, 峰松翼, 大久保文哉, 島田敬士

    第38回教育学習支援情報システム研究会(CLE38)  2022.11 

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    Country:Japan  

  • 学習支援システム間横断学習分析のための教育データ関連分析手法

    松尾早一朗, 峰松翼, 谷口雄太, 大久保文哉, 島田敬士

    第38回教育学習支援情報システム研究会(CLE38)  2022.11 

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    Country:Japan  

  • 学習状況に応じた学習記事検索手法の開発

    岡井成遊, 峰松翼, 大久保文哉, 谷口雄太, 島田敬士

    第39回教育学習支援情報システム研究会(CLE39)  2023.3 

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    Country:Japan  

  • 圃場センシング情報の推移から注目期間を捉える圃場環境ダイジェストシステムによる農業教育支援

    志賀寛羽, 峰松翼, 谷口雄太, 大久保文哉, 島田敬士

    第39回教育学習支援情報システム研究会(CLE39)  2023.3 

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    Country:Japan  

  • 視線情報による高解像度な学習ログの生成システムの開発

    後藤健, 峰松翼, 谷口雄太, 大久保文哉, 島田敬士

    第40回教育学習支援情報システム研究会(CLE40)  2023.6 

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    Country:Japan  

  • 教育データの分散表現生成手法の提案とAt-risk学生検知への応用

    宮崎佑馬, 峰松翼, 谷口雄太, 大久保文哉, 島田敬士

    第40回教育学習支援情報システム研究会(CLE40)  2023.6 

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    Country:Japan  

  • 生成AIを用いたシラバス情報の拡張と授業内トピック間類似度評価の検討

    尾崎真大, 大久保文哉, 峰松翼, 島田敬士

    第41回教育学習支援情報システム研究会(CLE41)  2023.12 

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    Country:Japan  

  • 数学問題文を入力とした3Dモデル自動生成システムの検討

    尾崎真大, 宮脇智也, 中村優吾, 部谷修平, 大久保文哉, 島田敬士

    第42回教育学習支援情報システム研究会(CLE42)  2024.3 

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    Country:Japan  

  • Students' Performance Prediction Based on Similarity between Online Textbooks and Questions

    REN Yongle, TANG Cheng, TANIGUCHI Yuta, MINEMATSU Tsubasa, OKUBO Fumiya, SHIMADA Atsushi

    第42回教育学習支援情報システム研究会(CLE42)  2024.3 

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    Country:Japan  

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MISC

  • 数学問題文を入力とした3Dモデル自動生成システムの検討

    尾崎真大, 宮脇智也, 中村優吾, 部谷修平, 大久保文哉, 島田敬士

    情報処理学会研究報告(Web)   2024 ( CLE-42 )   2024

  • Preface

    Flanagan B., Shimada A., Okubo F., Tseng H.T., Yang A.C.M., Lu O.H.T., Ogata H.

    CEUR Workshop Proceedings   3667   1 - 2   2024   ISSN:16130073

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    Publisher:CEUR Workshop Proceedings  

    Scopus

  • Students’ Performance Prediction based on Similarity between Online Textbooks and Questions

    REN Yongle, TANG Cheng, TANIGUCHI Yuta, MINEMATSU Tsubasa, OKUBO Fumiya, SHIMADA Atsushi

    情報処理学会研究報告(Web)   2024 ( CLE-42 )   2024

  • 圃場センシング情報の推移から注目期間を捉える圃場環境ダイジェストシステムによる農業教育支援

    志賀寛羽, 峰松翼, 谷口雄太, 大久保文哉, 島田敬士

    情報処理学会研究報告(Web)   2023 ( CLE-39 )   2023

  • 学習状況に応じた学習記事検索手法の開発

    岡井成遊, 峰松翼, 大久保文哉, 谷口雄太, 島田敬士

    情報処理学会研究報告(Web)   2023 ( CLE-39 )   2023

  • 教育データの分散表現生成手法の提案とAt-risk学生検知への応用

    宮崎佑馬, 峰松翼, 谷口雄太, 大久保文哉, 島田敬士

    情報処理学会研究報告(Web)   2023 ( CLE-40 )   2023

  • 生成AIを用いたシラバス情報の拡張と授業内トピック間類似度評価の検討

    尾崎真大, 大久保文哉, 峰松翼, 島田敬士

    情報処理学会研究報告(Web)   2023 ( CLE-41 )   2023

  • 視線情報による高解像度な学習ログの生成システムの開発

    後藤健, 峰松翼, 谷口雄太, 大久保文哉, 島田敬士

    情報処理学会研究報告(Web)   2023 ( CLE-40 )   2023

  • Attention機構を用いた背景変動に頑健な変化検出手法の分析

    濱田龍之介, 峰松翼, 谷口雄太, 大久保文哉, 島田敬士

    画像センシングシンポジウム講演資料集(Web)   29th   2023

  • Preface

    Flanagan B., Majumdar R., Li H., Shimada A., Okubo F., Ogata H., Akçapınar G., Lu M., Minematsu T., Hasnine M.N., Ono Y., Ocheja P., Takami K., Kuromiya H., Dai Y.

    CEUR Workshop Proceedings   3120   2022   ISSN:16130073

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    Publisher:CEUR Workshop Proceedings  

    Scopus

  • Learning Student Representations Focusing on Programming Processes

    谷口雄太, 峰松翼, 大久保文哉, 島田敬士

    情報処理学会研究報告(Web)   2022 ( CLE-38 )   2022

  • 学習テーマとその関連テーマによるデジタル教材のダイジェスト資料生成

    玉城亮治, 峰松翼, 谷口雄太, 大久保文哉, 島田敬士

    情報処理学会研究報告(Web)   2022 ( CLE-37 )   2022

  • 学習支援システム間横断学習分析のための教育データ関連分析手法

    松尾早一朗, 峰松翼, 谷口雄太, 大久保文哉, 島田敬士

    情報処理学会研究報告(Web)   2022 ( CLE-38 )   2022

  • 授業外学習支援のためのデジタル教材の自動要約

    島田 敬士, 大久保 文哉, 殷 成久, 緒方 広明

    サイバーメディア・フォーラム   2018.3

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    Language:Japanese  

    特集「ラーニング・アナリティクス最前線」

    DOI: 10.18910/70428

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Professional Memberships

  • Information Processing Society of Japan

    2022.2 - Present

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  • The Institute of Electronics, Information and Communication Engineers

    2016.8 - Present

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  • The Institute of Electronics, Information and Communication Engineers

  • Information Processing Society of Japan

  • Research Council of Evidence-Driven Education

Committee Memberships

  • 電子情報通信学会 九州支部   支部委員   Domestic

    2024.4 - 2025.4   

  • 情報処理学会 教育学習支援情報システム(CLE)研究会   Steering committee member   Domestic

    2022.4 - Present   

  • The 22nd International Conference on Developments in Language Theory   Program Commitee  

    2018   

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  • The 1st workshop on e-Book-based Educational Big Data for Enhancing Teaching and Learning   Program Commitee  

    2015   

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Academic Activities

  • Program Committee International contribution

    The 14th International Conference on Learning Analytics & Knowledge  ( Japan ) 2024.3

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    Type:Competition, symposium, etc. 

  • Organizing Committee International contribution

    The 6th Workshop on Predicting Performance Based on the Analysis of Reading Behavior  ( その他 ) 2024.3

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    Type:Competition, symposium, etc. 

  • モデレーター

    九州大学ラーニングアナリティクスセンター第2回シンポジウム  ( Japan ) 2023.9

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    Type:Competition, symposium, etc. 

  • 情報処理学会論文誌「教育とコンピュータ」(TCE)

    2023.4 - Present

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    Type:Academic society, research group, etc. 

  • 実行委員

    情報処理学会 教育学習支援情報システム(CLE)第39回研究会  ( Japan ) 2023.3

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    Type:Competition, symposium, etc. 

  • Organizing Committee International contribution

    The 5th Workshop on Predicting Performance Based on the Analysis of Reading Behavior  ( その他 ) 2023.3 - 2022.3

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    Type:Competition, symposium, etc. 

  • Screening of academic papers

    Role(s): Peer review

    2023

     More details

    Type:Peer review 

    Number of peer-reviewed articles in foreign language journals:3

    Proceedings of International Conference Number of peer-reviewed papers:5

  • Organizing Committee International contribution

    The 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior  ( その他 ) 2022.3

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    Type:Competition, symposium, etc. 

  • Screening of academic papers

    Role(s): Peer review

    2022

     More details

    Type:Peer review 

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

    Proceedings of International Conference Number of peer-reviewed papers:1

  • Screening of academic papers

    Role(s): Peer review

    2021

     More details

    Type:Peer review 

    Number of peer-reviewed articles in foreign language journals:1

  • Program Commitee International contribution

    The 22nd International Conference on Developments in Language Theory  ( Tokyo Japan ) 2018.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    The 1st workshop on e-Book-based Educational Big Data for Enhancing Teaching and Learning  ( Japan ) 2015.11 - 2015.12

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    Type:Competition, symposium, etc. 

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Research Projects

  • Construction and Evaluation of a High-Density Learning Analytics Infrastructure for Data-Driven Education

    Grant number:22H00551  2022 - 2025

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

    島田 敬士, 峰松 翼, 谷口 雄太, 大久保 文哉, 山下 隆義

      More details

    Authorship:Coinvestigator(s)  Grant type:Scientific research funding

    本研究は,学習者の学習状況を学習トピック単位で理解することで個別最適な情報推薦を行うデータ駆動型教育を実現する.①教育学習を学習トピック単位で記録する密センシング手法,②教育学習活動の文脈を把握する深層的活動分析手法,③行動変容を促す説得性を持ったフィードバック手法について研究を進める.大学教育の実践の場でデータ駆動PDCAサイクルの実証実験を実施し,研究成果の有用性と有効性を検証するとともに,成果を国内外に広く展開する.

    CiNii Research

  • Development and Evaluation of Learning Analytics Platform based on Learning Improvement Model

    Grant number:22H00552  2022 - 2025

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

    山田 政寛, 合田 美子, 谷口 雄太, 大久保 文哉, Lu Min

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    Authorship:Coinvestigator(s)  Grant type:Scientific research funding

    本研究は学習行動の改善を目的に, 学習効果を高めることを実証した教育・学習理論に基づいて、学習行動を連続性のある行動として捉え、学習行動の改善モデルの構築を行う。 そのモデルに従い、学習分析基盤の開発・評価を行う。この評価結果を踏まえて、学習行動の改善を促進させるための学習データ分析基盤のデザインモデルの構築を行う。

    CiNii Research

  • Development and evaluation of learning analytics dashboard for the decision-making support of learning behavior improvement

    Grant number:21KK0184  2021 - 2023

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Fostering Joint International Research (B)

    山田 政寛, 合田 美子, Hasnine Nehal, 大久保 文哉

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    Authorship:Coinvestigator(s)  Grant type:Scientific research funding

    本国際共同研究ではUniversity of MichiganのDr./Prof. Stephanie Teasleyとの共同研究において、学習者自身が様々な学習データの分析結果を踏まえて、学習態度と行動の改善意思決定行動を支援する学習ダッシュボードを開発し、その評価を行う。海外渡航としては、代表者が研究期間全体で1年半程度、分担者が1か月から2か月程度の渡航、それらを通じて、若手育成につながる研究の遂行を行う。その過程において、学習者の学習行動改善に関する意思決定に関するモデルの構築を行い、そのモデルに従った学習データ分析結果の可視化等のインターフェース開発も行い、評価する。

    CiNii Research

  • Deterministic computation in chemical reaction automata

    Grant number:16K16008  2016 - 2019

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Young Scientists (B)

    Okubo Fumiya

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    Authorship:Principal investigator  Grant type:Scientific research funding

    In order to clarify the properties of deterministic models in multiset rewriting systems in which the computation for an input symbolic sequence is uniquely determined, we defined determinism for chemical reaction automata in which reactions are applied sequentially. As a fundamental property, we found that the accepted language of deterministic chemical reaction automata is an incomparable relation with a well-known formal language, the context-free language. In order to synthesize deterministic chemical reaction automata that achieve the desired behavior, we defined the synthesis and decomposition of chemical reaction automata, and clarified the conditions under which they can be realizeed.

    CiNii Research

  • 学習ログを用いた学習過程のモデル化と分析

    2015

    教育研究プログラム・研究拠点形成プロジェクト(P&P), FSタイプ[若手教員支援]

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

  • ビッグデータの教育分野における利活用アプリケーションの研究開発, 副題:ビッグデータの教育分野における利活用アプリケーションの研究開発, 代表者:安浦 寛人

    2014 - 2016

    情報通信研究機構(NICT), ソーシャル・ビッグデータ利活用・基盤技術の研究開発, 課題A

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    Authorship:Coinvestigator(s)  Grant type:Contract research

  • ティーチングポートフォリオの開発と導入

    2014 - 2016

    教育の質向上支援プログラム(EEP)

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    Authorship:Coinvestigator(s)  Grant type:On-campus funds, funds, etc.

  • 化学反応オートマトンの解析と応用に関する研究

    Grant number:13J03528  2013

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for JSPS Fellows

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    Authorship:Principal investigator  Grant type:Scientific research funding

  • 化学反応オートマトンの解析と応用に関する研究

    2013

    Japan Society for the Promotion of Science  Research Fellowships for Young Scientists

      More details

    Authorship:Principal investigator  Grant type:Joint research

  • 化学反応オートマトンの研究

    Grant number:24700304  2012

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Young Scientists (B)

      More details

    Authorship:Principal investigator  Grant type:Scientific research funding

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Educational Activities

  • システム情報科学府において,研究指導等を行っている.

Class subject

  • 情報科学

    2024.10 - 2025.3   Second semester

  • 【通年】情報理工学研究Ⅰ

    2024.4 - 2025.3   Full year

  • 【通年】情報理工学講究

    2024.4 - 2025.3   Full year

  • 【通年】情報理工学演習

    2024.4 - 2025.3   Full year

  • 情報理工学読解

    2024.4 - 2024.9   First semester

  • 情報理工学論議Ⅰ

    2024.4 - 2024.9   First semester

  • 情報理工学論述Ⅰ

    2024.4 - 2024.9   First semester

  • 人工知能Ⅱ

    2023.12 - 2024.2   Winter quarter

  • 人工知能Ⅱ

    2023.12 - 2024.2   Winter quarter

  • 人工知能

    2023.10 - 2024.3   Second semester

  • 人工知能

    2023.10 - 2024.3   Second semester

  • 情報科学

    2023.10 - 2024.3   Second semester

  • 情報理工学論議Ⅱ

    2023.10 - 2024.3   Second semester

  • 情報理工学論述Ⅱ

    2023.10 - 2024.3   Second semester

  • 情報理工学演示

    2023.10 - 2024.3   Second semester

  • 電気情報工学セミナーA

    2023.10 - 2023.12   Fall quarter

  • 人工知能Ⅰ

    2023.10 - 2023.12   Fall quarter

  • 人工知能Ⅰ

    2023.10 - 2023.12   Fall quarter

  • 【通年】情報理工学講究

    2023.4 - 2024.3   Full year

  • 【通年】情報理工学研究Ⅰ

    2023.4 - 2024.3   Full year

  • 【通年】情報理工学演習

    2023.4 - 2024.3   Full year

  • 情報理工学論議Ⅰ

    2023.4 - 2023.9   First semester

  • 工学概論(Ⅰ群)

    2023.4 - 2023.9   First semester

  • 情報理工学読解

    2023.4 - 2023.9   First semester

  • 情報理工学論述Ⅰ

    2023.4 - 2023.9   First semester

  • 情報理工学論議Ⅱ

    2022.10 - 2023.3   Second semester

  • 人工知能

    2022.10 - 2023.3   Second semester

  • 情報科学

    2022.10 - 2023.3   Second semester

  • 人工知能

    2022.10 - 2023.3   Second semester

  • 情報理工学演示

    2022.10 - 2023.3   Second semester

  • 情報理工学論述Ⅱ

    2022.10 - 2023.3   Second semester

  • 情報理工学講究

    2022.4 - 2023.3   Full year

  • 情報理工学研究Ⅰ

    2022.4 - 2023.3   Full year

  • 情報理工学演習

    2022.4 - 2023.3   Full year

  • 情報理工学論議Ⅰ

    2022.4 - 2022.9   First semester

  • 情報理工学読解

    2022.4 - 2022.9   First semester

  • 情報理工学論述Ⅰ

    2022.4 - 2022.9   First semester

  • 電気情報工学入門

    2022.4 - 2022.6   Spring quarter

  • 電気情報工学入門

    2022.4 - 2022.6   Spring quarter

  • 人工知能

    2021.10 - 2022.3   Second semester

  • 情報理工学演示

    2021.10 - 2022.3   Second semester

  • 人工知能

    2021.10 - 2022.3   Second semester

  • 人工知能

    2021.10 - 2022.3   Second semester

  • 情報理工学演習

    2021.4 - 2022.3   Full year

  • 情報理工学研究Ⅰ

    2021.4 - 2022.3   Full year

  • 情報理工学読解

    2021.4 - 2021.9   First semester

  • 情報科学

    2017.10 - 2018.3   Second semester

  • 基幹教育セミナー

    2017.6 - 2017.8   Summer quarter

  • 情報科学

    2017.4 - 2017.9   First semester

  • 基幹教育セミナー

    2017.4 - 2017.9   First semester

  • 情報科学

    2017.4 - 2017.9   First semester

  • 課題協学A

    2016.10 - 2017.3   Second semester

  • 基幹教育セミナー(火5)

    2016.4 - 2016.9   First semester

  • 基幹教育セミナー(金5)

    2016.4 - 2016.9   First semester

  • 課題協学A

    2015.10 - 2016.3   Second semester

  • 基幹教育セミナー(金5)

    2015.4 - 2015.9   First semester

  • 基幹教育セミナー(火5)

    2015.4 - 2015.9   First semester

  • 公共放送とコミュニケーション~NHK福岡放送局との連携授業~

    2014.10 - 2015.3   Second semester

  • 基幹教育セミナー(火5)

    2014.4 - 2014.9   First semester

  • 基幹教育セミナー(金5)

    2014.4 - 2014.9   First semester

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FD Participation

  • 2023.7   Role:Participation   Title:【シス情FD】若手教員の研究紹介⑨

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2023.4   Role:Participation   Title:【シス情FD】若手教員による研究紹介⑧

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2023.1   Role:Speech   Title:九州大学ラーニングアナリティクスセンター第1回シンポジウム「理想のラーニングアナリティクスを⽬指して:研究と実践の往還」

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2022.11   Role:Participation   Title:【工学・シス情】教職員向け知的財産セミナー(FD)

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2022.9   Role:Speech   Title:全学FD「M2Bシステムの使い方 ~新機能を中心に紹介~」

    Organizer:University-wide

  • 2022.9   Role:Participation   Title:【シス情FD】研究機器の共用に向けて

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2022.7   Role:Participation   Title:【シス情FD】若手教員による研究紹介⑤

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2022.6   Role:Participation   Title:【シス情FD】電子ジャーナル等の今後について

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2022.5   Role:Participation   Title:【シス情FD】若手教員による研究紹介④「量子コンピュータ・システム・アーキテクチャの研究~道具になることを目指して~」

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2022.4   Role:Participation   Title:【シス情FD】第4期中期目標・中期計画等について

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2022.3   Role:Speech   Title:新M2Bシステムの使い方 ~新機能を中心に紹介します~(3/17)

    Organizer:University-wide

  • 2022.3   Role:Speech   Title:新M2Bシステムの使い方 ~新機能を中心に紹介します~(3/14)

    Organizer:University-wide

  • 2022.3   Role:Speech   Title:新M2Bシステムの使い方 ~新機能を中心に紹介します~(3/17)

    Organizer:University-wide

  • 2022.3   Role:Speech   Title:新M2Bシステムの使い方 ~新機能を中心に紹介します~(3/14)

    Organizer:University-wide

  • 2021.12   Role:Participation   Title:【シス情FD】企業出身教員から見た大学

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2021.11   Role:Participation   Title:【シス情FD】若手教員による研究紹介 及び 研究費獲得のポイント等について③

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2021.9   Role:Participation   Title:博士後期課程の充足率向上に向けて

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2021.7   Role:Participation   Title:若手教員による研究紹介 及び 科研取得のポイント、その他について ②

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2021.4   Role:Participation   Title:第1回全学FD(新任教員の研修)

    Organizer:University-wide

  • 2021.4   Role:Participation   Title:オンライン授業実施の"いろは"

    Organizer:University-wide

  • 2021.4   Role:Participation   Title:新任教員教育セミナー

    Organizer:University-wide

  • 2017.4   Role:Speech   Title:基幹教育セミナーFD

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2017.3   Role:Participation   Title:基幹教育院FD

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2016.3   Role:Participation   Title:基幹教育院FD

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2016.3   Role:Participation   Title:基幹教育セミナーFD

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2015.3   Role:Participation   Title:基幹教育セミナーFD

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2014.7   Role:Participation   Title:新GPA制度実施のためのFD

    Organizer:University-wide

  • 2014.4   Role:Participation   Title:基幹教育セミナー実践FD

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2014.4   Role:Participation   Title:第1回全学FD(新任教員の研修)

    Organizer:University-wide

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Other educational activity and Special note

  • 2022  Class Teacher  学部