The prevalence of shallow reading in online digital learning is steadily increasing, which has sparked interest in revealing the mechanisms behind shallow reading behavior, especially analyzing the causal relationship between its constituent features and learning performance. However, current causal analysis methods have many limitations in terms of experimental conditions, data independence assumptions, and analysis costs. Drawing on the application experience of Markov chain theory in the field of causality, this study adopts the structure-agnostic model (SAM) algorithm to design the structure, parameter loss, and learning process, and proposes an evaluation method for causal exploration based on generative adversarial neural networks (GANs). The study shows that the proposed maximum mean diversity (MMD) optimization method improves the stability of the model analysis results and clarifies that reading speed is a key factor in the occurrence of shallow reading behavior.

This study aims to investigate the relationship between students' assignment submission behavior and final grades in information literacy education using a large volume of learning logs stored on the LMS. A total of 12,516 freshman students participated in this study from the year 2018 to 2022, across the COVID-19 pandemic. The students were divided into high, medium, and low performance groups using k-means clustering. The results of the characteristics analysis show a significant early submission behavioral trend and a late submission behavioral trend in high and low performance groups, respectively. The on-demand class format during the COVID-19 pandemic resulted in more consistent early submission behavior for high performance students and late submission behavior for low performance students, respectively. The findings suggest that time management skill is a critical factor in both blended and online learning environments, affecting weekly submission behavior and final grades.

Reading rate is the number of words a user reads per unit of time, also known as reading speed. As online learning has been continuously growing, research on tracking learning behavior is undertaken, and the reading rate was beginning to be applied to measure the learning process. However, current calculating methods are based on the traditional learning environment of a paper-based course, and further research is needed to determine whether they can be used to track students’ learning in an online learning environment and integrate with their online reading habits. To this end, this study proposed a new method based on online learning used to calculate reading rate as a measure to track students’ learning status, after analyzing students’ reading behavior in the e-book learning system. This method not only can identify students with learning difficulties through reading rate outliers, but also provide urgent feedback to students on their reading status.

As a kind of data that can reflect learning status, e-book logs have been widely used in learning analytics, especially for the prediction of academic performance. However, the best prediction model cannot be found without determining the contribution of e-book logs to the prediction performance of the model and its creation process. To this end, this study used the scikit-learn, a free software machine learning library, to analyze learning performance of 234 participants by learning behavior logs, which were collected by an e-book system. Finally, six prediction models containing Decision Tree, Random Forests, XGBoost, Logistic Regression, Support Vector Machines, and K-nearest Neighbors were created. Also, the contribution of e-book logs on the establishment of different prediction models was obtained by three feature importance calculation methods, i.e., the impurity-based feature importance, coefficients feature importance, and permutation feature importance. Based on statistical results, it was concluded that the Decision Tree and Random Forests had the best prediction performance, which was compared to the other four models, with prediction performance scores ranging from 0.7 to 0.8. Besides, the four data features of Prev, Highlight, Maker, and Next were found to have the greatest impact on model prediction creation.

In this paper, we developed a digital textbook content improvement system, which can help teachers to find and improve the relevant content of digital textbook objectively based on the contents of quizzes. Based on the DITeL system, we have designed two modules of the system configuration. The first part includes the functions such as registration, modifications and deletions of the contents of questions, optional items and students’ answers, and the calculation and display of average grades ratio about questions which have been answered by students. The second part is based on the question and the correct answer, automatically to search relevant page about the digital textbook. We aim to use the system that students can take quizzes in every lesson to test the level of mastery of their knowledge. And teachers can find which questions that students got lower scores, and which the contents of relevant page about the digital textbook need to be improved.

Learning log is defined as a digital record of what learners have learned in their daily lives using ubiquitous technologies. By using the ubiquitous learning system named SCROLL(System for Capturing and Remining Of Learning Logs), learners can save what they have learned in their daily lives with photo, such as location (latitude and longitude), learning place, and date and time of creation as a learning log. Although learners have many opportunities to learn words and meanings of objects with taking a photo in their daily lives, SCROLL is not implemented functions for supporting language learning with object and text detection. Therefore, this paper proposes a ubiquitous learning system to support language learning with object and text detection technologies.

In this paper, we describe the support system for job-hunting students to learn job-hunting related terms using an eBook and a chat system. Job-hunting process is very unique and complicated in Japan. Job-hunting students face difficulties in many phases. Some job-hunting related terms are not used in daily conversation and very new to them. Therefore, it is necessary to support them. In fact, many universities in Japan have started providing their students with career education. The objective of this study is to examine whether or not the use of our chat system was effective in learning job-hunting related terms. The result of the evaluation showed there was no statistically significant difference. However the highest score was given when they were asked it's helpfulness.

With e-learning systems gradually being implemented, researchers worldwide have started devoting increasing attention to Learning Analytics. At Kobe university, a digital textbook reading system has been developed to collect learning logs in the face-to-face classroom. In a previous study, k-means clustering was implemented to analyze learning behavior patterns; however, there were problems such as few variables for clustering and a failure to consider weighting of the learning elements. Therefore, in this study we applied clustering by increasing the number of learning elements and assigned weights to the learning elements, then analyzed the learning behavioral patterns. We found some behavioral patterns of students who can save learning time if they effectively write memos and add markers.

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.

The group formation problem is a key problem in group learning. In this paper, we proposed a group formation system by using learning logs, which were collected from digital books system and Moodle system. We described the design and the usage of the system. In the future, we will evaluate the effective of this system.

We currently utilize the Moodle learning management system for teachers and students who participate in the course ‘College of Liberal Arts and Sciences’ at Kobe University in Japan. Digital textbooks, reports, quizzes and questionnaires in this course were administered using Moodle. In this paper, we proposed to use quizzes to measure and evaluate those digital textbooks recorded on Moodle. At the beginning of our study, we examined the questions that students got lower scores, and then we found the related teaching materials of digital textbooks and feedback to the teachers in order to improve the content of these digital textbooks.

This paper discusses learning behavior analysis using a learning management system (LMS) and an e-textbook system. We collected a large number of operation logs from e-textbooks to analyze the process of learning. In addition, we conducted a quiz to check the level of understanding. In our study, we especially focus on an analysis of the relationship between learning behavior in informal learning and its effectiveness in the corresponding quiz. We apply a machine learning and classification methodology for behavior analysis. Our experimental results demonstrate that students who undertake good informal learning achieve better scores in quizzes.

This preliminary research investigates the relationship between psychometric data and learning behaviors in the learning analytics research field, specifically, the relationship between self-regulated learning and learning behavior. The results of this limited research show that marker and annotation use have a weak significant relationship with self-efficacy and the intrinsic value of learning materials.

Analyses on students’ learning behaviors comprise an important thrust in education research. This study focused on e-books system used in the classroom and this system recorded students’ learning logs in their daily academic life. These learning logs can be used to analysis students’ learning behaviors. By performing partial correlation analysis, the study found that a number of learning behaviors have a significant relation with students’ test scores.

Although previous research has demonstrated the benefits of the “learning by searching” strategy, there is a new problem which is how to measure and analyze the effectiveness of "Learning by Searching" behaviors. In this paper, by using the record of the students’ learning history, we have proposed a SNSearch system to analyze student web-searching behaviors of "Learning by Searching".

Nowadays, it is a hot topic to analyze the huge amount of data in the world. This issue also exists in the learning during students’ life. The learning data are collected only to record students’ learning status. As a result, most learning data are not used to improve the quality of learning for students. In this paper, we propose an order made education system, which can recommend students to select the courses they want to learn. In order to analyze students’ learning behaviors, we collect students’ learning data by using mobile devices.

Kyushu University installed a private cloud system, named “campus cloud system”, using VCL and CloudStack. For a graduate school exercise course on web search engine, the authors prepared a virtual machine on VCL, which had apache web server and GETA indexer preinstalled. This paper introduces an outline of the cloud system, the exercise, and also reports advantages and disadvantages of cloud based education.

It is necessary to perform a literature review before starting a new research project. However, many students do not know the procedures of performing a literature review. In this paper, based on the professional experiences and opinions of expert researchers, we describe an SNS-based literature review system to help students conduct research surveys. This system includes two search engines, one is an article search engine, which can help students conduct research surveys, and the other is a logging search engine, which allows students to learn from each other via their logs and share experience with other students. User models of the system as well as its functions are presented.

A wrapper is a program that selectively extracts a necessary part (component) from Web pages. Automatic or semi-automatic wrapper construction is crucial to achieve a fine grained search engine for Web pages. However, this is not an easy task to achieve. This paper proposes a component-based search engine in which the content components gain a high score in the search results. Thus, the required segments for a query can be obtained without using a wrapper.

We constructed a support system for research trend surveys not only to accelerate the preliminary step but also to let students have a better grips of trend progresses and keyword transitions. Our system dynamically searches relevant words that are frequently used in the targeted academic field and gives users effective visualizations to understand trend transitions.

This paper proposes a Social Networking Service site based mobile environment for learning foreign languages called SONLEM, which supports learners to find a partner who can solve the language learning problems at the online community, and an appropriate request chain of friends will be recommended upon their request. The learner can practice his second language with a native speaker who is learning his language.

The continuous development of wireless and mobile technologies has allowed the creation of an additional platform for supporting learning, one that can be embedded in the same physical space in which the learning is taking place. This paper describes a computer supported ubiquitous learning environment for language learning, called LOCH (Languagelearning Outside the Classroom with Handhelds). In the environment, the teacher assigns field activities to the students, who go around the town to fulfill them and share their individual experiences. The main aim of this project, called One Day Trip with PDA, was to integrate the knowledge acquired in the classroom and the real needs of the students in their daily life.

During learning computer science theory, it is essential to learn sorting algorithms, but it is not easy to understand the concept of the different sorting algorithms. This paper describes a system called PSSLSA (PDA-based Participatory Simulation System for Learning Sorting Algorithms). This is an interactive simulation system to learn the sorting algorithms. Learners use it to deeply understand the sorting algorithms. Using this system, the teacher can assign tasks to his student and ask them to sort a list of numbers according to a certain algorithm. Learners receive these tasks, collaborate together and send the result to the server. The system will check it and feedback the student with the positions of the numbers if there is a mistake. The learners will correct the number positions and send it back to the server. Learners can understand the algorithm through the dissections and their errors.