||Chengjiu Yin, 緒方広明, 矢野米雄, JAPELAS: Supporting Japanese Polite Expressions Learning Using PDA towards Ubiquitous Learning, Journal of Information and Systems in Education, 3, 1, 33-39, 2005.01.
||Yuqin Liu, Chengjiu Yin, Hiroaki Ogata, Guojun Qiao, Yoneo Yano, A FAQ-based e-learning environment to support Japanese language learning, International Journal of Distance Education Technologies, 10.4018/jdet.2011070104, 9, 3, 45-55, 2011.07, In traditional classes, having many questions from learners is important because these questions indicate difficult points for learners and for teachers. This paper proposes a FAQ-based e-Learning environment to support Japanese language learning that focuses on learner questions. This knowledge sharing system enables learners to interact and share information and knowledge through FAQ and e-mail. Teachers contribute answers to discussion among learners. The system also allows learners to discuss and collaborate, stimulating their motivation to study Japanese as a foreign language. All questions are stored in a FAQ database, allowing other learners to reuse resources, helping learners learn by themselves and reduce teacher workloads. Copyright © 2011, IGI Global..
||Chengjiu Yin, Sachio Hirokawa, Jane Yin Kim Yau, Kiyota Hashimoto, Yoshiyuki Tabata, Tetsuya Nakatoh, Research trends with cross tabulation search engine, International Journal of Distance Education Technologies, 10.4018/jdet.2013010103, 11, 1, 31-44, 2013.01, To help researchers in building a knowledge foundation of their research fields which could be a timeconsuming process, the authors have developed a Cross Tabulation Search Engine (CTSE). Its purpose is to assist researchers in 1) conducting research surveys, 2) efficiently and effectively retrieving information (such as important researchers, research groups, keywords), and also 3) providing analytical information relating to past and current research trends in a particular field. Their CTSE system employs data-processing technologies and emphasizes the use of a "Learn by Searching" learning strategy to support students to analyze such research trends. To show the effectiveness of CTSE, a pilot experiment has been conducted, where participants were assigned to do research survey tasks and then answer a questionnaire regarding the effectiveness and usability of the system. The results showed that the system has been helpful to students in conducting research surveys, and the research trend transitions that our system presented were effective for producing research trend surveys. Moreover, the results showed that most students had favorable attitudes toward the usage and usability of the system, and those students were satisfied in gaining more know ledge in a particular research field in a short period. Copyright © 2013, IGI Global..
||Chengjiu Yin, Yanjie Song, Yoshiyuki Tabata, Hiroaki Ogata, Gwo-Jen Hwang, Developing and Implementing a Framework of Participatory Simulation for Mobile Learning Using Scaffolding, EDUCATIONAL TECHNOLOGY & SOCIETY, 16, 2, 137-150, 2013.04, This paper proposes a conceptual framework, scaffolding participatory simulation for mobile learning (SPSML), used on mobile devices for helping students learn conceptual knowledge in the classroom. As the pedagogical design, the framework adopts an experiential learning model, which consists of five sequential but cyclic steps: the initial stage, concrete experience, observation and reflection, abstract conceptualization, and testing in new situations. Goal-based and scaffolding approaches to participatory simulations are integrated into the design to enhance students' experiential learning. Using the SPSML framework, students can experience the following: (1) learning in augmented reality by playing different participatory roles in mobile simulations in the micro-world on a mobile device, and (2) interacting with people in the real world to enhance understanding of conceptual knowledge. An example of the SPSML-based system was implemented and evaluated. The experimental results show that the system was conducive to the students' experiential learning and motivation. Moreover, the students who learned with the proposed approach gained significantly higher accuracy rates in performing the more complicated sorting algorithm..
||Gwo-Jen Hwang, Hui-Chun Chu, Chengjiu Yin, Hiroaki Ogata, Transforming the educational settings: innovative designs and applications of learning technologies and learning environments, INTERACTIVE LEARNING ENVIRONMENTS, 10.1080/10494820.2014.998863, 23, 2, 127-129, 2015.03.
||Hyo Jeong So, Xiaoqing Gu, Tzu Chien Liu, Chengjiu Yin, Special Issue on: "Technology-Transformed Learning: Reflections and Future Research Agendas", International Journal of Mobile Learning and Organisation, 9, 285-288, 2015.01.
||Gwo Jen Hwang, Hui Chun Chu, Chengjiu Yin, Objectives, methodologies and research issues of learning analytics, Interactive Learning Environments, 10.1080/10494820.2017.1287338, 25, 2, 143-146, 2017.02, Objectives, methodologies and research issues of learning analytics.
||Chengjiu Yin, Jane Yin Kim Yau, Gwo Jen Hwang, Hiroaki Ogata, An SNS-based model for finding collaborative partners, Multimedia Tools and Applications, 10.1007/s11042-015-2480-1, 76, 9, 11531-11545, 2017.05, This paper proposes a model, Recommendation of Appropriate Partners (RAP), used on a Social Networking Service (SNS) for locating appropriate “helpers” for users based on individual users’ Chain of Friends (CoF) relationships. Using the RAP model, individual users can participate in a collaborative online community in remote locations, whereby helpers are willing to help other users solve their tasks/problems, and it is intended that both the users and helpers gain knowledge from these interactive online sessions. An example of the RAP-based system was implemented to invite Program Committee members to an international conference. The system was evaluated and the experimental results show that our model is very effective for discovering collaboration partners and finding users with similar interests in order to create communities for providing future and longer-term helping exchange..
||Chengjiu Yin, Neil Yen, Qun Jin, Special Issue on Trends and Research Issues of Emerging Technologies to Enhance Learning, INTERNATIONAL JOURNAL OF DISTANCE EDUCATION TECHNOLOGIES, 16, 4, 2018.10.
||Atsushi Shimada, Fumiya Okubo, Chengjiu Yin, Hiroaki Ogata, Automatic Summarization of Lecture Slides for Enhanced Student Preview-Technical Report and User Study, IEEE Transactions on Learning Technologies, 10.1109/TLT.2017.2682086, 11, 2, 165-178, 2018.04, This paper is an extension of research originally reported in . 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..
||Yuxi Jin, Ping Li, Wenxiao Wang, Suiyun Zhang, Di Lin, Chengjiu Yin, GAN-based pencil drawing learning system for art education on large-scale image datasets with learning analytics, Interactive Learning Environments, 10.1080/10494820.2019.1636827, 2019.07, We design a generative adversarial network (GAN)-based pencil drawing learning system for art education on large image datasets to help students study how to draw pencil drawings for images and scenes. The system generates a pencil drawing result for a natural image based on GAN. The GAN network is trained on pencil drawing big datasets containing image pairs of natural images and their corresponding pencil drawings. Using the pencil drawing learning system, students can paint pencil drawings whenever they want and for whatever they like by uploading an image of the content they want to draw and getting a pencil drawing example of the uploaded image from the system. With the returned pencil drawing, students will see the pencil drawing effect of natural scenes clearly and realize how to draw the pencil drawing for the natural scene. Besides, with students using the pencil drawing learning system, it will be convenient for teachers assigning homework to students. Teachers can know the learning demands of students by evaluating the hand-in homework and update the content correspondingly. We have conducted two user studies for evaluating the practicality of the system, and the result of the two user studies demonstrated the applicability and practicality of the system..
||C. C. Hsiao, Jeff C. H. Huang, Anna Y. Q. Huang, Owen H. T. Lu, C. J. Yin, Stephen J. H. Yang, Exploring the effects of online learning behaviors on short-term and long-term learning outcomes in flipped classrooms, Interactive Learning Environments, 10.1080/10494820.2018.1522651, 27, 8, 1-18, 2019.11, The flipped classroom pedagogy has been widely used recently. Despite many researches have paid attention with the learning outcome of flipped classroom, there has been limited attention in regard to investigate the relationship between learning behavior and learning outcomes in a flipped classroom. In this paper, we proposed to investigate the influence of online learning behaviors on short-term and long-term learning outcomes in a flipped classroom. This study used Calculus and grade point average (GPA) scores to represent short-term and long-term learning outcomes, respectively. Multiple linear regression indicated that students’ online learning behavior does not have a significant effect on short-term learning outcomes, but has a significant effect on long-term learning outcomes. For applying multiple correspondence analysis, students were divided into groups according to five grade levels based on their scores. According to GPA grade level, students’ online learning behaviors had a significant effect on long-term learning outcomes for the five groups (GPAa, GPAb, GPAc, GPAd, GPAe). According to their Calculus grade level, students’ online learning behaviors had a significant effect on short-term learning outcomes for three groups (CALa, CALd, and CALe), but two groups (CALb and CALc) did not demonstrate this trend. For exploring the effects of online learning behaviors on future learning outcomes, GPA can be considered representative because the GPA was calculated for the entire academic year 2015. Students in the CALa group exhibited the highest frequency of online learning behaviors and obtained the highest GPA grade levels (GPAa and GPAb). For the CALb, CALc, CALd, and CALe groups, students with a higher frequency of online learning behaviors obtained a higher GPA grade level. These results indicate that students’ online learning behaviors have a positive effect on future learning outcomes..
||Chengjiu Yin, Masanori Yamada, Misato Oi, Atsushi Shimada, Fumiya Okubo, Kentaro Kojima, Hiroaki Ogata, Exploring the Relationships between Reading Behavior Patterns and Learning Outcomes Based on Log Data from E-Books: A Human Factor Approach, International Journal of Human-Computer Interaction, 10.1080/10447318.2018.1543077, 35, 4-5, 313-322, 2019.03, 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..
||Tosti Hsu-Cheng Chiang, Stephen J, H. Yang, Chengjiu Yin, Effect of gender differences on 3-on-3 basketball games taught in a mobile flipped classroom, Interactive Learning Environments (Journal), 10.1080/10494820.2018.1495652, 27, 8, 1-13, 2019.11.
||Anna Y. Q. Huang, Owen H. T. Lu, Jeff C. H. Huang, C. J. Yin, Stephen J. H. Yang, Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs, Interactive Learning Environments (Journal), 10.1080/10494820.2019.1636086, 28, 2, 1-25, 2020.02, In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model, the reason which affected the performance of the model was overlooked. This study collected seven datasets within three universities located in Taiwan and Japan and listed performance metrics of risk identification model after fed data into eight classification methods. U1, U2, and U3 were used to denote the three universities, which have three, two, and two cases of datasets (learning logs), respectively. According to the results of this study, the factors influencing the predictive performance of classification methods are the number of significant features, the number of categories of significant features, and Spearman correlation coefficient values. In U1 dataset case 1.3 and U2 dataset case 2.2, the numbers of significant features, numbers of categories of significant features, and Spearman correlation coefficient values for significant features were all relatively high, which is the main reason why these datasets were able to perform classification with high predictive ability..
||Bo Jiang, Simin Wu, Chengjiu Yin, Haifeng Zhang, Knowledge Tracing Within Single Programming Practice Using Problem-Solving Process Data, IEEE Transactions on Learning Technologies, 10.1109/TLT.2020.3032980, 13, 4, 822-832, 2020.10, Accurately tracing the state of learner knowledge contributes to providing high-quality intelligent support for computer-supported programming learning. However, knowledge tracing is difficult when learners have only had a few practice opportunities, which is often common in block-based programming. This article proposed two knowledge tracing models that can exploit the problem-solving process data generated by learners from a single programming task. A novel metric, the approaching index, was developed using the tree edit distance in abstract syntax trees to measure the similarities between the learners' intermediate solutions and the optimal solution. The proposed method allows for each learner's programming path to be represented as a raw approaching index sequence (AISeq) or as a single variable (AIScore) by averaging the AISeq. A logistic regression model was first designed to predict the learners' performances using their AIScore, the number of attempts, and their current performance. A second model, a recurrent neural network model, was also developed to directly use the AISeq and to make predictions. To verify the effectiveness of these models, a series of statistical analyses and experiments were conducted on two existing large-scale block-based programming datasets, the results from which revealed that the proposed models were competitive with four state-of-the-art models on multiple metrics, such as the precision-recall curve, accuracy, specificity, and Cohen's Kappa. Especially, the proposed models were found to be more robust than the compared models in predicting who would fail to complete the tasks..
||Nian-Shing Chen, Chengjiu Yin, Pedro Isaias, Joseph Psotka, Educational big data: extracting meaning from data for smart education, INTERACTIVE LEARNING ENVIRONMENTS, 10.1080/10494820.2019.1635395, 28, 2, 142-147, 2020.02, Educational big data: extracting meaning from data for smart education.
||Kousuke Mouri, Fumiya Suzuki, Atsushi Shimada, Noriko Uosaki, Chengjiu Yin, Keiichi Kaneko, Hiroaki Ogata, Educational data mining for discovering hidden browsing patterns using non-negative matrix factorization, Interactive Learning Environments, 10.1080/10494820.2019.1619594, 29, 7, 1-13, 2021.10, This paper describes a method to collect data of which section of pages learners were browsing in digital textbooks without eye-tracking technologies. In previous researches on digital textbook systems, it was difficult to collect such data without using eye-tackers. However, eye-trackers cost a massive budget. Our proposed system automatically hides the texts in the digital textbooks with mask processing before the learners browse the texts in the digital textbooks. If they click the hidden texts, the system gets rid of the masks and the texts appear letter by letter. We used NMF to discover learners’ browsing patterns from the collected logs. Evaluation experiments were conducted to examine the effectiveness of our system in terms of fascination, understandableness and enhancement of thinking and to discover learners’ browsing patterns. It was found that our method could enhance thinking skills. A browsing pattern of diligent learners with high learning achievements was also found..
||Kousuke Mouri, Noriko Uosaki, Mohammad Hasnine, Atsushi Shimada, Chengjiu Yin, Keiichi Kaneko, Hiroaki Ogata, An automatic quiz generation system utilizing digital textbook logs, Interactive Learning Environments, 10.1080/10494820.2019.1620291, 29, 5, 1-14, 2021.07, This paper describes an automatic quiz generation system designed to support language learning that utilizes digital textbook logs. Learners often memorize words in digital textbooks while preparing for an examination, and they often use the highlight function for the words. Previous studies regarding annotations and highlights have shown that learning only by using the highlight function on important content in textbooks did not affect learning achievements. Therefore, in this study, we developed a system that can support the repeated learning by analyzing digital textbook logs and providing appropriate quizzes. An evaluation experiment involving 31 international students was conducted to assess whether the quizzes provided by our proposed system are able to enhance the learning achievements as compared to teacher-created quizzes. The results show that the quizzes by our proposed system and the teacher-created quizzes were both equally effective. A correlation analysis was conducted to identify the correlation among the learning achievements, the number of quizzes, and each variable in questionnaires. We found that there is a positive correlation between the number of quizzes and the students’ learning achievements..
||Vivien Lin, Gi-Zen Liu, Gwo-Jen Hwang, Nian-Shing Chen, Chengjiu Yin, Outcomes-based appropriation of context-aware ubiquitous technology across educational levels., Interact. Learn. Environ., 10.1080/10494820.2019.1703012, 30, 8, 1515-1538, 2022.07, This review study investigates the appropriation of sensing technology in context-aware ubiquitous learning (CAUL) in the fields of sciences, engineering, and humanities. 40 empirical studies with concrete learning outcomes across mandatory and higher education have been systematically reviewed and thematically analyzed with an outcomes-based teaching and learning approach. Four derived themes have been found to describe the design and implementation of CAUL, including learner-centeredness, technological facilitation, learning ecology, and research evaluation. The learning processes enabled by context-aware sensing technology have been explicated, revealing specific ways to apply new technologies in formal and informal environments. The analysis based on intended learning outcomes suggest that more efforts should be directed to fostering competence in analyzing and creating in mandatory education, and to creating in tertiary settings. Finally, unequal distribution of CAUL implementation across world regions calls for more technological appropriation in Southeast Asia and Africa. Specific suggestions on how to improve CAUL are also provided to better prepare learners in the twenty-first century..
||CHENGJIU YIN, Han-Yu Sung, Gwo-Jen Hwang, Sachio Hirokawa, Hui Chun Chu, Brendan Flanagan, Yoshiyuki Tabata, Learning by Searching: a Learning Approach that Provides Searching and Analysis Facilities to Support Research Trend Surveys, Journal of Educational Technology & Society, 16, 3, 286-300, 2013.12, With the popularity of the Internet, online searching is becoming an important part of learning. In this paper, based on the “Learning by Searching” theory, a learning environment is developed, which includes a search engine to assist students in recognizing the progression of trends and keyword transitions for specific domains. To efficiently support research trend surveys, an automatic data accumulation and classification approach is proposed to construct the database excerpts instead of manual keyword registration or any other heuristic preprocesses. With an associative search module, the search engine dynamically searches for relevant words that are frequently used in the targeted academic field, and provides learners with effective visualizations to understand the trend transitions. An experiment has been conducted on a college information management course to show the effectiveness of the proposed approach. The experiment results show that the students who learned with the new approach had significantly better learning performance in terms of recognizing the trend transitions of the targeted issues than those who learned with conventional search engines..
||CHENGJIU YIN, Yanjie Song, Yoshiyuki Tabata, Hiroaki Ogata, Gwo-Jen Hwang, Developing and Implementing a Framework of Participatory Simulation for Mobile Learning Using Scaffolding, Journal of Educational Technology & Society, 16, 2, 137-150, 2013.04, This paper proposes a conceptual framework, scaffolding participatory simulation for mobile learning (SPSML),
used on mobile devices for helping students learn conceptual knowledge in the classroom. As the pedagogical
design, the framework adopts an experiential learning model, which consists of five sequential but cyclic steps:
the initial stage, concrete experience, observation and reflection, abstract conceptualization, and testing in new
situations. Goal-based and scaffolding approaches to participatory simulations are integrated into the design to
enhance students’ experiential learning. Using the SPSML framework, students can experience the following: (1)
learning in augmented reality by playing different participatory roles in mobile simulations in the micro-world
on a mobile device, and (2) interacting with people in the real world to enhance understanding of conceptual
knowledge. An example of the SPSML-based system was implemented and evaluated. The experimental results
show that the system was conducive to the students’ experiential learning and motivation. Moreover, the
students who learned with the proposed approach gained significantly higher accuracy rates in performing the
more complicated sorting algorithm..
||CHENGJIU YIN, Sachio Hirokawa, Jane Yin-Kim YAU, Tetsuya Nakatoh, Kiyota Hashimoto, Yoshiyuki Tabata, Analyzing Research Trends with Cross Tabulation Search Engine, Special Issue on: "International Workshop on Technology-Enhanced Social Learning", Vol 11. No.1, pp. 31- 44, 2013., 11, 1, 31-44, 2013.03, For junior researchers who are just beginning their research journeys, it is very important for them to conduct a research survey to collect the information required for them to build a knowledge foundation of the field, and using this information to guide the planning phases of their research projects. This is often a time-consuming process for junior and senior researchers alike. As a result, we have developed a Cross Tabulation Search Engine (CTSE). The purpose of this engine is to assist researchers to 1) conduct research surveys, 2) retrieve efficiently and effectively information (such as important researchers, research groups, keywords), and also 3) provide analytical information relating to past and current research trends in a particular field. Our CTSE system employs data-processing technologies and emphasizes the use of a "Learn by Searching" learning strategy to support students to analyze such research trends, for example, by comparing the number of papers (retrieved by the system) to determine which country has been more actively in the specified research area. To show the effectiveness of CTSE, a pilot experiment has been conducted, where participants were arranged to do some research survey tasks and then answer a questionnaire regarding the effectiveness and usability of the system. The experimental results showed that the system has been helpful to students in conducting research surveys, and the research trend transitions that our system presented were effective for producing research trend surveys. Moreover, results from the questionnaires showed that most students had favorable attitudes toward the usage and usability of the system. In summary, the study results showed that by using the CTSE system, it was possible that students could gain more knowledge in a particular research field as well as in different research fields, in a short period of time. Additionally, students commented that they wanted to use the system in the future for assisting them to conduct research. The implementation of this system and the study results are elaborated on in this paper. As part of our future work, we plan to extend the system for promoting social collaborative learning activities between individual learners. .
||CHENGJIU YIN, Hiroaki Ogata, Yoshiyuki Tabata, Yoneo Yano, Supporting the Acquisition of Japanese Polite Expressions in Context-Aware Ubiquitous Learning, Int. J. Mobile Learning and Organisation, 4, 2, 214-234, 2010.05.
||Chengjiu Yin, Hiroaki Ogata and Yoneo Yano, Participatory Simulation Framework to Support Learning Computer Science, International Journal of Mobile Learning and Organisation, Vol.1, No.3, pp.288-304, 2007.09.
||Chengjiu Yin，Hiroaki Ogata，and Yoneo Yano, JAPELAS: Supporting Japanese Polite Expressions Learning Using PDA towards Ubiquitous Learning, The Journal of Information and Systems in Education, Vol．3, No．1, pp．33-39, 2005.01.