Updated on 2024/12/02

Information

 

写真a

 
TANIGUCHI YUTA
 
Organization
Research Institute for Information Technology Associate Professor
Graduate School of Integrated Frontier Sciences Department of Library Science(Concurrent)
Title
Associate Professor
Profile
・プログラミング学習支援 プログラミング演習授業における学習者の学習活動ログデータを利用して、学習者および教師へのサポートを行う。 ・構成的学習支援環境 容易に組み合わせ可能な学習支援環境のデザインにより、柔軟な学習環境の構成と一貫性ある学習ログの記録を実現する。

Research Areas

  • Informatics / Learning support system

Degree

  • Ph. D

Research History

  • 九州大学 情報基盤研究開発センター 准教授

    2023.4 - Present

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  • 九州大学 情報基盤研究開発センター 助教

    2020.12 - 2023.3

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  • The University of Tokushima

Research Interests・Research Keywords

  • Research theme:Learning Analytics

    Keyword:Learning Analytics

    Research period: 2024

  • Research theme:data mining

    Keyword:data mining

    Research period: 2024

  • Research theme:Learning Analytics

    Keyword:Learning Analytics

    Research period: 2016.5

Awards

  • 2022 年度 山下記念研究賞

    2023.3   情報処理学会   谷口雄太, 峰松翼, 大久保文哉, 島田敬士, “プログラミング演習の軌跡:学生のコーディング過程理解のための教師支援”

  • Best Paper Award

    2019.11   The 16th International Conference on Cognition and Exploratory Learn- ing in Digital Age (CELDA2019)  

  • 優秀デモンストレーション賞

    2013.6   日本情報科教育学会第6回全国大会   iPad を用いた「デジたま講座」教材・教具の開発

Papers

  • Exploring Behavioral and Strategic Factors Affecting Secondary Students' Learning Performance in Collaborative Problem Solving-Based STEM Lessons

    Chen, L; Taniguchi, Y; Shimada, A; Yamada, M

    SAGE OPEN   14 ( 2 )   2024.4   ISSN:2158-2440

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    Publisher:SAGE Open  

    Despite the growing emphasis on integrating collaborative problem-solving (CPS) into science, technology, engineering, and mathematics (STEM) education, a comprehensive understanding of the critical factors that affect the effectiveness of this educational approach remains a challenge. This study aims to identify effective strategic and behavioral factors in course design and assess how these factors contribute to students’ learning performance. This study collected data from 106 students enrolled in seventh-grade science classes by using a mixed-method approach. First, the t-test results indicate that students’ learning performance was improved through CPS-based STEM learning. A path analysis shows that CPS awareness and several behavioral factors had direct effects, while several strategic factors had indirect effects on the improvement of learning performance. Finally, a dialog analysis indicates that students’ integrative use of CPS skills, especially task regulation skills used along with other skills, helped improve learning performance. This study not only bridges the gap in understanding the effectiveness of CPS in STEM education but also provides specific suggestions for improving instructional design.

    DOI: 10.1177/21582440241251641

    Web of Science

    Scopus

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

    Tang, C; Chen, L; Li, G; Minematsu, T; Okubo, F; Taniguchi, Y; Shimada, A

    KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, PT II, KSEM 2024   14885   192 - 203   2024   ISSN:2945-9133 ISBN:978-981-97-5494-6 eISSN:1611-3349

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    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

    Web of Science

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

    Leelaluk, S; Tang, C; Minematsu, T; Taniguchi, Y; Okubo, F; Yamashita, T; Shimada, A

    IEEE ACCESS   12   100659 - 100675   2024   ISSN:2169-3536

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    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

    Web of Science

    Scopus

  • 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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  • 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.

    Scopus

  • Cross-language font style transfer Reviewed

    Chenhao Li, Yuta Taniguchi, Min Lu, Shin’ichi Konomi, Hajime Nagahara

    Applied Intelligence   53 ( 15 )   18666 - 18680   2023.8   ISSN:0924-669X eISSN:1573-7497

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    Abstract

    In this paper, we propose a cross-language font style transfer system that can synthesize a new font by observing only a few samples from another language. Automatic font synthesis is a challenging task and has attracted much research interest. Most previous works addressed this problem by transferring the style of the given subset to the content of unseen ones. Nevertheless, they only focused on the font style transfer in the same language. In many cases, we need to learn font style from one language and then apply it to other languages. Existing methods make this difficult to accomplish because of the abstraction of style and language differences. To address this problem, we specifically designed the network into a multi-level attention form to capture both local and global features of the font style. To validate the generative ability of our model, we constructed an experimental font dataset of 847 fonts, each containing English and Chinese characters with the same style. Results show that our model generates 80.3% of users’ preferred images compared with state-of-the-art models.

    DOI: 10.1007/s10489-022-04375-6

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    Other Link: https://link.springer.com/article/10.1007/s10489-022-04375-6/fulltext.html

  • 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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    Publishing type:Research paper (scientific journal)  

    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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    Publishing type:Research paper (scientific journal)  

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

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

    Journal of Educational Computing Research   2023.1

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

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

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

    IEEE Transactions on Learning Technologies   2023.1

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

  • 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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    Publishing type:Research paper (international conference proceedings)  

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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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  • 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)  

    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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  • Contrastive Learning for Reading Behavior Embedding in E-book System

    Minematsu, T; Taniguchi, Y; Shimada, A

    ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2023   13916   426 - 437   2023   ISSN:2945-9133 ISBN:978-3-031-36271-2 eISSN:1611-3349

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

    When students use e-learning systems such as learning management systems and e-book systems, the operation logs are stored and analyzed to understand student learning behaviors. For implementing some applications, such as dashboard systems and at-risk student detection, the operation logs are mainly transformed into features designed by researchers. Such hand-crafted features, like the number of operations, are easily interpretable. However, the power of the hand-craft features may be limited for the recent large-scale educational dataset. In machine learning research, data-driven features are demonstrated to be a better representation than hand-crafted features. However, there are few discussions in the educational data due to a need for many operation logs. In this study, we collect reading logs of an e-book system. We propose a representation learning method for the reading logs based on contrastive learning. Our proposed method transforms time-series reading logs into reading behavior feature vectors directly without hand-crafted features. In our experiments, we demonstrate that the power of our feature representation is better than a traditional count-based hand-crafted feature representation in the at-risk student detection task. In addition, we investigate the characteristics of the feature space learned by our proposed method.

    DOI: 10.1007/978-3-031-36272-9_35

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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)  

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

    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.

    Scopus

  • 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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    Publishing type:Research paper (international conference 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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  • 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

     More details

    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.

    Scopus

  • 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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    Publishing type:Research paper (international conference proceedings)  

    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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  • 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

     More details

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

    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.

    Scopus

  • 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

     More details

    Publishing type:Research paper (international conference proceedings)  

    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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  • 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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    Publishing type:Research paper (scientific journal)  

    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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    Publishing type:Research paper (scientific journal)   Publisher:MDPI AG  

    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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  • Exploring jump back behavior patterns and reasons in e-book system

    Boxuan Ma, Min Lu, Yuta Taniguchi, Shin’ichi Konomi

    Smart Learning Environments   9 ( 1 )   2022.1   eISSN:2196-7091

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    Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    <title>Abstract</title>With the increasing use of digital learning materials in higher education, the accumulated operational log data provide a unique opportunity to analyzing student learning behaviors and their effects on student learning performance to understand how students learn with e-books. Among the students’ reading behaviors interacting with e-book systems, we find that jump-back is a frequent and informative behavior type. In this paper, we aim to understand the student’s intention for a jump-back using user learning log data on the e-book materials of a course in our university. We at first formally define the “jump-back” behaviors that can be detected from the click event stream of slide reading and then systematically study the behaviors from different perspectives on the e-book event stream data. Finally, by sampling 22 learning materials, we identify six reading activity patterns that can explain jump backs. Our analysis provides an approach to enriching the understanding of e-book learning behaviors and informs design implications for e-book systems.

    DOI: 10.1186/s40561-021-00183-6

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    Other Link: https://link.springer.com/article/10.1186/s40561-021-00183-6/fulltext.html

  • 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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    Publisher:Proceedings - 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022  

    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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    Publisher:CEUR Workshop Proceedings  

    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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  • 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   ISBN:978-1-4503-9573-1

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    Publishing type:Research paper (international conference proceedings)   Publisher:ACM  

    DOI: 10.1145/3506860.3506915

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

  • 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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  • 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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  • 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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  • 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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Presentations

  • Investigating Programming Performance Predictability from Embedding Vectors of Coding Behaviors International conference

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

    ICCE2023  2023.12 

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    Event date: 2023.12

    Language:English  

    Country:Japan  

  • Contrastive Learning for Reading Behavior Embedding in E-book System International conference

    Tsubasa Minematsu, Yuta Taniguchi, and Atsushi Shimada

    AIED2023  2023.7 

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    Event date: 2023.7

    Language:English  

    Country:Japan  

  • データが可能にするプログラミング学習プロセス理解への新しい視点

    谷口雄太

    情報処理学会 IPSJ-ONE 2023  2023.3 

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    Event date: 2023.3

    Language:Japanese  

    Country:Japan  

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

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

    ICCE2022  2022.12 

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    Event date: 2022.12

    Language:English  

    Country:Japan  

  • Detection of At-Risk Students in Programming Courses International conference

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

    ICCE2022  2022.12 

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    Event date: 2022.12

    Language:English  

    Country:Japan  

  • Assessment of At-Risk Students’ Predictions from E-Book Activities Representations in Practical Applications International conference

    2022.12 

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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発表会 (CLE38)  2022.10 

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    Event date: 2022.10

    Language:Japanese  

    Country:Japan  

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

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

    WCCE2022  2022.8 

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    Event date: 2022.8

    Language:English  

    Country:Japan  

  • A System to Realize Time- and Location-Independent Teaching and Learning among Learners through Learning-Articles International conference

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

    WCCE2022  2022.8 

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    Event date: 2022.8

    Language:English  

    Country:Japan  

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

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

    The 12th International Conference on Learning Analytics & Knowledge  2022.3 

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    Event date: 2022.3

    Language:English  

  • Coding Trajectory Map: Student Programming Situations Made Visually Locatable International conference

    Yuta Taniguchi, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada

    The 12th International Conference on Learning Analytics & Knowledge  2022.3 

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    Event date: 2022.3

    Language:English  

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

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

    The 12th International Conference on Learning Analytics & Knowledge  2022.3 

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    Event date: 2022.3

    Language:English  

  • Encoding Students Reading Characteristics to Improve Low Academic Performance Predictive Models International conference

    Erwin Daniel Lopez Zapata, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    The 12th International Conference on Learning Analytics & Knowledge  2022.3 

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    Event date: 2022.3

    Language:English  

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

    Erwin Daniel Lopez Zapata, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada

    The 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior  2022.3 

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    Event date: 2022.3

    Language:English  

  • Predicting Student Performance Based on Lecture Materials Data Using Neural Network Models International conference

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

    The 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior  2022.3 

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    Event date: 2022.3

    Language:English  

  • Generating Travel Recommendations for Older Adults Based on Their Social Media Activities International conference

    Yuhong Lu, Yuta Taniguchi, Shin'ichi Konomi

    The 13th International Conference on Cross-Cultural Design  2021.7 

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    Event date: 2021.7

    Language:English  

  • Composing Learning Environments with e-Textbook System International conference

    Yuta Taniguchi, Tsubasa Minematsu, Atsushi Shimada

    Third Workshop on Intelligent Textbooks  2021.6 

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    Event date: 2021.6

    Language:English  

  • 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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    Event date: 2021.5

    Language:Japanese  

  • 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  

  • 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  

  • Exploration and Explanation: An Interactive Course Recommendation System for University Environments International conference

    Boxuan Ma, Min Lu, Yuta Taniguchi, Shin'ichi Konomi

    The 4th Workshop on Exploratory Search and Interactive Data Analytics  2021.4 

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    Event date: 2021.4

    Language:English  

  • Few-shot Font Style Transfer between Different Languages International conference

    Chenhao Li, Yuta Taniguchi, Min Lu, Shin'ichi Konomi

    IEEE/CVF Winter Conference on Applications of Computer Vision  2021.1 

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    Event date: 2021.1

    Language:English  

  • How to Design Collaborative Problem Solving-based STEM Lessons based on the Perspective of Learning Behaviors? International conference

    Li Chen, Yuta Taniguchi, Atsushi Shimada, Masanori Yamada

    IEEE International Conference on Engineering, Technology and Education  2020.12 

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    Language:English  

  • Development of a Visualization System to Enhance Participation in Computer-Supported Collaboration Learning International conference

    Yufan Xu, Yuta Taniguchi, Yoshiko Goda, Atsushi Shimada, Masanori Yamada

    IEEE International Conference on Engineering, Technology and Education  2020.12 

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    Event date: 2020.12

    Language:English  

  • Multi-level Attention Networks for Font Style Transfer between Different Languages

    Chenhao Li, Yuta Taniguchi, Min Lu, Shin'ichi Konomi

    Visual Computing 2020  2020.12 

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    Language:English  

  • Relationship between Learning Behaviors and Social Presence in Online Collaborative Learning International conference

    Yufan Xu, Yuta Taniguchi, Yoshiko Goda, Atsushi Shimada, Masanori Yamada

    The 17th International Conference on Cognition and Exploratory Learning in Digital Age  2020.11 

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    Event date: 2020.11

    Language:English  

  • The Normalized Impact Index for Keywords in Scholarly Papers to Detect Subtle Research Topics International conference

    Daisuke Ikeda, Yuta Taniguchi, Kazunori Koga

    The 8th International Workshop on Mining Scientific Publications  2020.8 

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    Event date: 2020.8

    Language:English  

  • Course Recommendation for University Environment International conference

    Boxuan Ma, Yuta Taniguchi, Shin'ichi Konomi

    The 13th International Conference on Educational Data Mining  2020.7 

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    Event date: 2020.7

    Language:English  

  • 構成的学習環境

    谷口雄太, 峰松翼, 島田敬士

    第31回教育学習支援情報システム研究発表会 (CLE31)  2020.5 

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    Language:Japanese  

  • Collecting and Integrating Multimodal Data from a Programming Exercise Environment International conference

    Yuta Taniguchi, Atsushi Shimada

    Integrating Multi-channel Learning Data to Model Complex Learning Processes  2020.3 

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    Event date: 2020.3

    Language:English  

  • Can the Area marked in eBook Readers Specify Learning Performance? International conference

    Yufan Xu, Xuewang Geng, Chen Li, Satomi Hamada, Yuta Taniguchi, Hiroaki Ogata, Atsushi Shimada, Masanori Yamada

    Data Challenge  2020.3 

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    Event date: 2020.3

    Language:English  

  • Understanding Jump Back Behaviors in E-book System International conference

    Boxuan Ma, Jiadong Chen, Chenhao Li, Likun Liu, Min Lu, Yuta Taniguchi, Shin'ichi Konomi

    Data Challenge  2020.3 

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    Language:English  

  • Exploring the Design Space for Explainable Course Recommendation Systems in University Environments International conference

    Boxuan Ma, Min Lu, Yuta Taniguchi, Shin'ichi Konomi

    Explainable Learning Analytics  2020.3 

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    Event date: 2020.3

    Language:English  

  • Examining Language-agnostic Methods of Automatic Coding in the Community of Inquiry Framework International conference

    Yuta Taniguchi, Shin'ichi Konomi, Yoshiko Goda

    The 16th International Conference on Cognition and Exploratory Learning in Digital Age  2019.11 

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    Event date: 2019.11

    Language:English  

  • K-Tips: Knowledge Extension Based on Tailor-made Information Provision System International conference

    Keita Nakayama, Atsushi Shimada, Tsubasa Minematsu, Yuta Taniguchi, Rin-Ichiro Taniguchi

    The 16th International Conference on Cognition and Exploratory Learning in Digital Age  2019.11 

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    Language:English  

  • Preliminary Analysis of Tweets to Predict Suicide Risks

    Yuhong Lu, Min Lu, Yuta Taniguchi, Shin'ichi Konomi

    The 72nd Joint Conference of Electrical, Electronics and Information Engineers in Kyushu  2019.9 

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    Event date: 2019.9

    Language:English  

  • Cleaning Massive Training Data for Intelligent Programming Support System

    Likun Liu, Yuta Taniguchi, Min Lu, Shin'ichi Konomi

    The 72nd Joint Conference of Electrical, Electronics andInformation Engineers in Kyushu  2019.9 

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    Event date: 2019.9

    Language:English  

  • Automatically Generate Slide Images Using Generative Adversarial Nets

    Chenhao Li, Yuta Taniguchi, Min Lu, Shin'ichi Konomi

    The 72nd Joint Conference of Electrical, Electronics and Informa-tion Engineers in Kyushu  2019.9 

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    Event date: 2019.9

    Language:English  

  • Exploring a Model for Evaluating Teaching Slides Based on Their Layouts

    Jiadong Chen, Yuta Taniguchi, Min Lu, Kohei Hatano, Shin'ichi Konomi

    The 72nd Joint Conference of Electrical,Electronics and Information Engineers in Kyushu  2019.9 

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    Event date: 2019.9

    Language:English  

  • Toward Automatic Identification of Dataset Names in Scholarly Articles International conference

    Daisuke Ikeda, Yuta Taniguchi

    The 8th International Conference on Data Science and Institutional Research  2019.7 

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    Event date: 2019.7

    Language:English  

  • Investigating Error Resolution Processes in C Programming Exercise Courses International conference

    Yuta Taniguchi, Atsushi Shimada, Shin'ichi Konomi

    The 12th International Conference on Educational Data Mining  2019.7 

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    Language:English  

  • Optimizing Assignment of Students to Courses based on Learning Activity Analytics International conference

    Atsushi Shimada, Kousuke Mouri, Yuta Taniguchi, Hiroaki Ogata, Rin-Ichiro Taniguchi, Shin'ichi Konomi

    The 12th International Conference on Educational Data Mining  2019.7 

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    Event date: 2019.7

    Language:English  

  • Recommending Highlights on Students' E-Textbooks International conference

    Yuta Taniguchi, Atsushi Shimada, Masanori Yamada, Shin'ichi Konomi

    The 30th conference of the Society for Information Technology and Teacher Education  2019.3 

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    Event date: 2019.3

    Language:English  

  • Page-wise Difficulty Level Estimation using e-Book Operation Logs International conference

    Tetsuya Shiino, Atsushi Shimada, Tsubasa Minematsu, Kohei Hatano, Yuta Taniguchi, Shin'ichi Konomi, Rinichiro Taniguchi

    The 9th International Conference on Learning Analytics & Knowledge  2019.3 

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    Event date: 2019.3

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  • Design a Course Recommendation System Based on Association Rule for Hybrid Learning Environments

    Boxuan Ma, Yuta Taniguchi, Shin'ichi Konomi

    2019.3 

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  • Learning Path Recommendation in University Environments Based on Sequence Mining

    Boxuan Ma, Yuta Taniguchi, Shin'ichi Konomi

    The 81th National Convention of IPSJ  2019.3 

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    Language:English  

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

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

    IEEE International Conference on Teaching, Assessment, and Learning for Engineering  2018.12 

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    Event date: 2018.12

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  • BR-Map: Concept Map System Using e-Book logs International conference

    Masanori Yamada, Atsushi Shimada, Misato Oi, Yuta Taniguchi, Shin'ichi Konomi

    The 15th International Conference on Cognition and Exploratory Learning in Digital Age  2018.10 

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    Event date: 2018.10

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  • E-Book Learner Behaviors Under Meaningful Learning Modes International conference

    Jingyun Wang, Fumiya Okubo, Yuta Taniguchi

    The 13th Multidisciplinary Academic Conference on Education, Teaching and Learning  2018.10 

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    Event date: 2018.10

    Language:English  

  • Comparative Analysis of Adaptive Learning Path Recommendation Algorithms

    Boxuan Ma, Yuta Taniguchi, Shin'ichi Konomi

    The 71st Joint Conference of Electrical, Electronics and Informa-tion Engineers in Kyushu  2018.9 

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    Event date: 2018.9

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  • Data-Driven Feedback for Students Based on Word Clouds

    Shiman Cui, Yuta Taniguchi, Shin'ichi Konomi

    The 71st Joint Conference of Electrical, Electronics and Information Engineers in Kyushu  2018.9 

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    Event date: 2018.9

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  • How are Students Struggling in Programming? Understanding Learning Processes from Multiple Learning Logs International conference

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

    The 11th International Conference on Educational Data Mining  2018.7 

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    Language:English  

  • Relation Analysis between Learning Activities on Digital Learning System and Seating Area in Classrooms International conference

    Atsushi Shimada, Fumiya Okubo, Yuta Taniguchi, Hiroaki Ogata, Rin-Ichiro Taniguchi, Shin'ichi Konomi

    The 11th International Conference on Educational Data Mining  2018.7 

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  • Instructional Design and Evaluation of Science Education to Improve Collaborative Problem Solving Skills International conference

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

    The 29th conference of the Society for Information Technology and Teacher Education  2018.3 

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    Event date: 2018.3

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  • Online Change Detection for Monitoring Individual Student Behavior via Clickstream Data on e-Book System International conference

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

    The 8th International Conference on Learning Analytics & Knowledge  2018.3 

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    Event date: 2018.3

    Language:English  

  • On the Prediction of Students' Quiz Score by Recurrent Neural Network International conference

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

    The 3rd Multimodal Learning Analytics Across Spaces Workshop  2018.3 

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    Event date: 2018.3

    Language:English  

  • Learning Style Based Collaborative Learning Construction: Can it Improve Group Work in a Learning Environment

    Yiduo Gao, Yuta Taniguchi, Shin'ichi Konomi, Kentaro Kojima, Atsushi Shimada, Hiroaki Ogata

    2018.1 

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  • Effects of Prior Knowledge of High Achievers on Use of e-Book Highlights and Annotations International conference

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

    The 25th International Conference on Computers in Education  2017.12 

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    Event date: 2017.12

    Language:English  

  • Analysis on Students' Usage of Highlighters on E-textbooks in Classroom International conference

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

    The 25th International Conference on Computers in Education  2017.12 

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    Language:English  

  • A Visualization System for Predicting Learning Activities Using State Transition Graphs International conference

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

    The 14th International Conference on Cognition and Exploratory Learning in Digital Age  2017.10 

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    Event date: 2017.10

    Language:English  

  • Exploring Students' Learning Journals with Web-based Interactive Report Tool International conference

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

    The 14th International Conference on Cognition and Exploratory Learning in Digital Age  2017.10 

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    Event date: 2017.10

    Language:English  

  • Towards a Learner-Centric Notification Environment for Multimodal Learning Platforms International conference

    Shin'ichi Konomi, Atsushi Shimada, Masanori Yamada, Fumiya Okubo, Yuta Taniguchi, Jingyun Wang

    Workshop on Multimodal Learning Analytics Across (Physical and Digital) Spaces  2017.9 

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    Event date: 2017.9

    Language:English  

  • 高等学校におけるラーニングアナリティックスに基づいた授業の試行

    山田政寛, 大久保文哉, 谷口雄太, 毛利考佑, 島田敬士, 大井京, 緒方広明, 井上功一, 木實新一

    第42回 教育システム情報学会 全国大会  2017.8 

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    Event date: 2017.8

    Language:Japanese  

  • Extracting Teaching Activities from E-book Logs Using Time-Series Shapelets

    Daiki Suehiro, Yuta Taniguchi, Atsushi Shimada, Hiroaki Oagata

    2017.8 

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    Event date: 2017.8

    Language:English  

  • Revealing Hidden Impression Topics in Students' Journals Based on Nonnegative Matrix Factorization International conference

    Yuta Taniguchi, Daiki Suehiro, Atsushi Shimada, Hiroaki Ogata

    IEEE 17th International Conference on Advanced Learning Technologies  2017.7 

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    Event date: 2017.7

    Language:English  

  • Face-to-Face Teaching Analytics: Extracting Teaching Activities from E-book Logs via Time-Series Analysis International conference

    Daiki Suehiro, Yuta Taniguchi, Atsushi Shimada, Hiroaki Ogata

    IEEE 17th International Conference on Advanced Learning Technologies  2017.7 

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    Event date: 2017.7

    Language:English  

  • Behavioral Analysis and Visualization on Learning Logs from the CALL Course

    Li Huiyong, Tsuchiya Tomoyuki, Suehiro Daiki, Taniguchi Yuta, Shimada Atsushi, Suzuki Yubun, Ohashi Hiroshi, Ogata Hiroaki

    2017.5 

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    Event date: 2017.5

    Language:English  

  • 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, Kentaro Kojima

    The 7th International Learning Analytics & Knowledge Conference  2017.3 

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    Event date: 2017.3

    Language:English  

  • Real-time Learning Analytics for C Programming Language Courses International conference

    Xinyu Fu, Atsushi Shimada, Hiroaki Ogata, Yuta Taniguchi, Daiki Suehiro

    The 7th International Learning Analytics & Knowledge Conference  2017.3 

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    Event date: 2017.3

    Language:English  

  • 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 信学技報 パターン認識・メディア理解研究会 (PRMU2016-105)  2016.10 

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    Event date: 2016.10

    Language:Japanese  

  • 研究者情報を活用したURA的産学連携手法について --- 研究者DBとしてのMATCIを活用した産学連携

    谷口雄太, 水野充, 堀内美穂, 斉藤卓也, 荒木寛幸

    RA協議会第1回年次大会  2015.9 

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    Event date: 2015.9

    Language:Japanese  

  • URAの研究者情報DBとして活用できるMATCIの紹介

    谷口雄太, 宮本賢治, 永冨太一, 土居修身, 井内健介, 下方晃博, 荒木寛幸

    RA協議会第1回年次大会  2015.9 

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    Event date: 2015.9

    Language:Japanese  

  • Discover Overlapping Topical Regions by Geo-semantic Clustering of Tweets International conference

    Yuta Taniguchi, Daiki Monzen, Lutfiana Sari Ariestien, Daisuke Ikeda

    The 8th International Symposium on Mining and Web  2015.3 

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    Event date: 2015.3

    Language:English  

  • iPadを用いた「デジたま講座」教材・教具の開発

    竹田正幸, 池田大輔, 谷口雄太, 脇田早苗, 池内昌子

    日本情報科教育学会第6回全国大会  2013.6 

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    Event date: 2013.6

    Language:Japanese  

  • Graph Clustering Based on Optimization of a Macroscopic Structure of Clusters International conference

    Yuta Taniguchi, Daisuke Ikeda

    The 14th International Conference on Discovery Science  2011.10 

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    Event date: 2011.10

    Language:English  

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MISC

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 教育の情報化で実現できるラーニング・アナリティクス Reviewed

    陳莉, 谷口雄太, 山田政寛

    教育と医学   2019.1

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    Language:Japanese  

  • ラーニングアナリティクス:2.大学における全学規模のラーニングアナリティクス Reviewed

    木實新一, 大久保文哉, 谷口雄太

    情報処理   2018.9

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    Language:Japanese  

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Industrial property rights

Patent   Number of applications: 0   Number of registrations: 0
Utility model   Number of applications: 0   Number of registrations: 0
Design   Number of applications: 0   Number of registrations: 0
Trademark   Number of applications: 0   Number of registrations: 0

Academic Activities

  • Screening of academic papers

    Role(s): Peer review

    2022

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    Type:Peer review 

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

    Number of peer-reviewed articles in Japanese journals:1

    Proceedings of International Conference Number of peer-reviewed papers:12

  • 座長

    2018年度電気・情報関係学会九州支部連合大会  2021.9

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    Type:Competition, symposium, etc. 

  • プログラム委員 International contribution

    2020.12

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    Type:Competition, symposium, etc. 

  • プログラム委員 International contribution

    2020.3

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    Type:Competition, symposium, etc. 

  • プログラム委員 International contribution

    2020.3

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    Type:Competition, symposium, etc. 

  • プログラム委員 International contribution

    2019.3

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    Type:Competition, symposium, etc. 

  • プログラム委員 International contribution

    2019.3

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    Type:Competition, symposium, etc. 

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Research Projects

  • 学習者を理解し導くことができる学習支援アルゴリズムの開発

    Grant number:24K15209  2024.4 - 2028.3

    科学研究費助成事業  基盤研究(C)

    谷口 雄太

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    Grant type:Scientific research funding

    学習者の活動から学習者の理解度やスキル、解答の意図などを推定する技術の開発と、推定結果をもとに学習者の理解や解答をより良いものへと導くためのフィードバック手法の開発を行う。また、実際の授業を通じて枠組みの有効性を評価する。

    CiNii Research

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

    Grant number:22H00552  2022.4 - 2026.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

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    Grant type:Scientific research funding

    CiNii Research

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

    Grant number:22H00551  2022.4 - 2026.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

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    Grant type:Scientific research funding

    CiNii Research

  • 学習状況に応じて動的に最適化される仮想的学習環境の構築

    2021.4 - 2024.3

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    Authorship:Principal investigator 

  • Developing a virtual learning environment dynamically optimized for students' circumstances

    Grant number:21K17863  2021 - 2023

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Early-Career Scientists

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    Authorship:Principal investigator  Grant type:Scientific research funding

    CiNii Research

  • 個別・協調学習の往還を支援するインタラクション高度化基盤の開発と評価

    2019.4 - 2023.3

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    Authorship:Coinvestigator(s) 

  • The evaluation and development of interactive learning environment for the enhancement of cycle between individual and collaborative learning

    Grant number:19H01716  2019 - 2022

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

    Yamada Masanori

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

    One of the major problems pointed out in CSCL practice and research has been the low quality of interaction, such as "social corner cutting," in which learners begin to cut corners on tasks due to the lack of visibility of the division of labor in the learning group. The purpose of this study was to identify system design elements that support the return of individual and cooperative learning, to develop and evaluate the system according to these elements, and to improve the interaction between learners. The results showed that the system developed based on the visualization elements of individual learning behavior extracted in this study promotes individual learning behavior and improves performance.

    CiNii Research

  • 学習者の体験をフィードバックとして顕在化させるプログラミング学習支援環境の開発

    2017.4 - 2021.3

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    Authorship:Principal investigator 

  • 学習者の体験をフィードバックとして顕在化させるプログラミング学習支援環境の開発

    Grant number:17K12804  2017 - 2020

    科学研究費助成事業  若手研究(B)

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    Authorship:Principal investigator  Grant type:Scientific research funding

  • 社会的共有調整学習理論に基づいたプロジェクト型学習支援システムの開発と評価

    2016.4 - 2019.3

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    Authorship:Coinvestigator(s) 

  • 社会的共有調整学習理論に基づいたプロジェクト型学習支援システムの開発と評価

    Grant number:16H03080  2016 - 2018

    日本学術振興会  科学研究費助成事業  基盤研究(B)

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

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Class subject

  • ライブラリーサイエンスPTLⅠ

    2024.10 - 2025.3   Second semester

  • 電子資料開発論

    2024.10 - 2025.3   Second semester

  • プログラミング演習(P)

    2024.6 - 2024.8   Summer quarter

  • 特別研究Ⅰ

    2024.4 - 2025.3   Full year

  • ライブラリーサイエンス特別演習

    2024.4 - 2025.3   Full year

  • ライブラリーサイエンス特別演習

    2024.4 - 2025.3   Full year

  • ライブラリーサイエンス特別研究

    2024.4 - 2025.3   Full year

  • ライブラリーサイエンス特別研究

    2024.4 - 2025.3   Full year

  • 特別研究Ⅱ

    2024.4 - 2025.3   Full year

  • 情報システム論

    2024.4 - 2024.9   First semester

  • ライブラリーサイエンス講究

    2024.4 - 2024.9   First semester

  • ライブラリーサイエンス講究

    2024.4 - 2024.9   First semester

  • 統合新領域最先端セミナー

    2024.4 - 2024.6   Spring quarter

  • 電子資料開発論

    2023.10 - 2024.3   Second semester

  • ライブラリーサイエンスPTLⅠ

    2023.10 - 2024.3   Second semester

  • プログラミング演習(P)

    2023.6 - 2023.8   Summer quarter

  • サイバーセキュリティ基礎論

    2023.4 - 2023.6   Spring quarter

  • サイバーセキュリティ基礎論

    2023.4 - 2023.6   Spring quarter

  • プログラミング演習(P)

    2022.12 - 2023.2   Winter quarter

  • サイバーセキュリティ基礎論

    2022.4 - 2022.6   Spring quarter

  • サイバーセキュリティ基礎論

    2022.4 - 2022.6   Spring quarter

  • 情報処理概論

    2021.6 - 2021.8   Summer quarter

  • プログラミング演習(P)

    2021.6 - 2021.8   Summer quarter

  • サイバーセキュリティ基礎論

    2021.4 - 2021.6   Spring quarter

  • サイバーセキュリティ基礎論

    2021.4 - 2021.6   Spring quarter

  • プログラミング演習

    2020.10 - 2021.3   Second semester

  • プログラミング演習

    2020.4 - 2020.9   First semester

  • プログラミング演習

    2019.10 - 2020.3   Second semester

  • 情報科学

    2018.10 - 2019.3   Second semester

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FD Participation

  • 2023.6   Role:Participation   Title:統合新領域学府 FD ライブラリーサイエンス 木土博成

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

  • 2023.5   Role:Participation   Title:統合新領域学府 FD 担当:廣田 正樹 学府長

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

  • 2020.10   Role:Participation   Title:2020年度 ユニバーシティ・デザイン・ワークショップの報告

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

  • 2020.9   Role:Participation   Title:電気情報工学科総合型選抜(AO入試)について

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

  • 2019.10   Role:Participation   Title:電子ジャーナルの現状と今後の動向に関する説明会

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

  • 2019.6   Role:Participation   Title:8大学情報系研究科長会議の報告

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

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