E-learning, which can be used anywhere and at any time, is very convenient and has been introduced to improve learning efficiency. However, securing a completion rate has been a major challenge. Recently, the learning forms of e-learning require learners to be introspective, deliberate, and logical and have proven to be incompatible with many learners with low completion rates. Thus, we developed an e-learning system that incorporates a fill-in-the-blank question-type concept map to deepen learners' understanding of learning contents while watching learning videos. The developed system promotes active learning reflectively and logically by allowing learners to answer blank question labels on concept maps from video content and labels associated with the blank question labels. We confirmed in the laboratory experiment by comparing with a conventional video-based learning system that the developed system encouraged a learner to do more system operations for rechecking the learning content and to better understand the learning contents while watching the learning video. As the next step, a field experiment is needed to investigate the usefulness and effectiveness of the developed system in actual environments in order to boost the practicality of the developed system. In this study, we introduced the developed system into the two class of the uviversity course and investigated the level of understanding to the learning contents, the system operations, and the usefulness of the developed system by comparing with those in the laboratory experiment. The results showed that the developed system provided to support the understanding to learning content and the usefulness of each function in the field experiment, as in the laboratory experiment. On the other hand, the students in the field experiment gave lower usefulness of the developed system than those in the lab experiment, suggesting that the students who attempted to thoroughly understand the learning contents in the field experiment were fewer than those in the lab experiment from their system operations during the learning.

The aim of a computer-aided drawing therapy system in this work is to associate drawings which a client makes with the client's mental state in quantitative terms. A case study is conducted on experimental data which contain both pastel drawings and mental state scores obtained from the same client in a psychotherapy program. To perform such association through colors, we translate a drawing to a color feature by measuring its representative colors as primary color rates. A primary color rate of a color is defined from a psychological primary color in a way such that it shows a rate of emotional properties of the psychological primary color which is supposed to affect the color. To obtain several informative colors as representative ones of a drawing, we define two kinds of color: approximate colors extracted by color reduction, and area-averaged colors calculated from the approximate colors. A color analysis method for extracting representative colors from each drawing in a drawing sequence under the same conditions is presented. To estimate how closely a color feature is associated with a concurrent mental state, we propose a method of utilizing machine-learning classification. A practical way of building a classification model through training and validation on a very small dataset is presented. The classification accuracy reached by the model is considered as the degree of association of the color feature with the mental state scores given in the dataset. Experiments were carried out on given clinical data. Several kinds of color feature were compared in terms of the association with the same mental state. As a result, we found out a good color feature with the highest degree of association. Also, primary color rates proved more effective in representing colors in psychological terms than RGB components. The experimentals provide evidence that colors can be associated quantitatively with states of human mind.

Recently, Global navigation satellite system (GNSS) positioning has been widely used in various applications (e.g. car navigation system, smartphone map application, autonomous driving). In GNSS positioning, coordinates are calculated from observed satellite signals. The observed signals contain various errors, so the calculated coordinates also have some errors. Double-difference is one of the widely used ideas to reduce the errors of the observed signals. Although double-difference can remove many kinds of errors from the observed signals, some errors still remain (e.g. multipath error). In this paper, we define the remaining error as “double-difference-error (DDE)” and propose a method for estimating DDE using machine learning. In addition, we attempt to improve DGNSS positioning by feeding back the estimated DDE. Previous research applying machine learning to GNSS has focused on classifying whether the signal is LOS (Line Of Sight) or NLOS (Non Line Of Sight), and there is no study that attempts to estimate the amount of error itself as far as we know. Furthermore, previous studies had the limitation that their dataset was recorded at only a few locations in the same city. This is because these studies are mainly aimed at improving the positioning accuracy of vehicles, and collecting large amounts of data using vehicles is costly. To avoid this problem, in this research, we use a huge amount of openly available stationary point data for training. Through the experiments, we confirmed that the proposed method can reduce the DGNSS positioning error. Even though the DDE estimator was trained only on stationary point data, the proposed method improved the DGNSS positioning accuracy not only with stationary point but also with mobile rover. In addition, by comparing with the previous (detect and remove) approach, we confirmed the effectiveness of the DDE feedback approach.

We consider network security exercises where students construct virtual networks with User-mode Linux (UML) virtual machines and then execute attack and defense activities on these networks. In an older version of the exercise system, the students accessed the desktop screens of the remote servers running UMLs with Windows applications and then built networks by executing UML commands. However, performing the exercises remotely (e.g., due to the COVID-19 pandemic) resulted in difficulties due to factors such as the dependency of the work environment on specific operating systems, narrow-band networks, as well as issues in providing support for configuring UMLs. In this paper, a novel web-based hands-on system with intuitive and seamless operability and lightweight responsiveness is proposed in order to allow performing the considered exercises while avoiding the mentioned shortcomings. The system provides web pages for editing device layouts and cable connections by mouse operations intuitively, web pages connecting to UML terminals, and web pages for operating X clients running on UMLs. We carried out experiments for evaluating the proposed system on the usability, system performance, and quality of experience. The subjects offered positive assessments on the operability and no negative assessments on the responsiveness. As for command inputs in terminals, the response time was shorter and the traffic was much smaller in comparison with the older system. Furthermore, the exercises using nano required at least 16 kbps bandwidth and ones using wireshark required at least 2048 kbps bandwidth.

In recent years, the use of big data has attracted more attention, and many techniques for data analysis have been proposed. Big data analysis is difficult, however, because such data varies greatly in its regularity. Heterogeneous mixture machine learning is one algorithm for analyzing such data efficiently. In this study, we propose online heterogeneous learning based on an online EM algorithm. Experiments show that this algorithm has higher learning accuracy than that of a conventional method and is practical. The online learning approach will make this algorithm useful in the field of data analysis.

This study focuses on computer based foreign language vocabulary learning systems. Our objective is to automatically build vocabulary decks with desired levels of relative difficulty relations. To realize this goal, we exploit the fact that word frequency is a good indicator of vocabulary difficulty. Subsequently, for composing the decks, we pose two requirements as uniformity and diversity. Namely, the difficulty level of the cards in the same deck needs to be uniform enough so that they can be grouped together and difficulty levels of the cards in different decks need to be diverse enough so that they can be grouped in different decks. To assess uniformity and diversity, we use rank-biserial correlation and propose an iterative algorithm, which helps in attaining desired levels of uniformity and diversity based on word frequency in daily use of language. In experiments, we employed a spaced repetition flashcard software and presented users various decks built with the proposed algorithm, which contain cards from different content types. From users' activity logs, we derived several behavioral variables and examined the polyserial correlation between these variables and difficulty levels across different word classes. This analysis confirmed that the decks compiled with the proposed algorithm induce an effect on behavioral variables in line with the expectations. In addition, a series of experiments with decks involving varying content types confirmed that this relation is independent of word class.

This article introduces our investigation on learning state estimation in e-learning on the condition that visual observation and recording of a learner's behaviors is possible. In this research, we examined methods of adaptation for a new learner for whom a small number of ground truth data can be obtained.

Physically Unclonable Function (PUF) is a cryptographic primitive that is based on physical property of each entity or Integrated Circuit (IC) chip. It is expected that PUF be used in security applications such as ID generation and authentication. Some responses from PUF are unreliable, and they are usually discarded. In this paper, we propose a new PUF-based authentication system that exploits information of unreliable responses. In the proposed method, each response is categorized into multiple classes by its unreliability evaluated by feeding the same challenges several times. This authentication system is named Q-class authentication, where Q is the number of classes. We perform experiments assuming a challenge-response authentication system with a certain threshold of errors. Considering 4-class separation for 4-1 Double Arbiter PUF, it is figured out that the advantage of a legitimate prover against a clone is improved form 24% to 36% in terms of success rate. In other words, it is possible to improve the tolerance of machine-learning attack by using unreliable information that was previously regarded disadvantageous to authentication systems.

Self-paced e-learning provides much more freedom in time and locale than traditional education as well as diversity of learning contents and learning media and tools. However, its limitations must not be ignored. Lack of information on learners' states is a serious issue that can lead to severe problems, such as low learning efficiency, motivation loss, and even dropping out of e-learning. We have designed a novel e-learning support system that can visually observe learners' non-verbal behaviors and estimate their learning states and that can be easily integrated into practical e-learning environments. Three pairs of internal states closely related to learning performance, concentration-distraction, difficulty-ease, and interest-boredom, were selected as targets of recognition. In addition, we investigated the practical problem of estimating the learning states of a new learner whose characteristics are not known in advance. Experimental results show the potential of our system.

Massive Open Online Courses (MOOC) have been invented to support Virtual Learning Environment (VLE) for higher education. While numerous learning courses and contents were authored, most of the existing resources are now hard to reuse/redistribute among instructors due to the privacy of the contents. Therefore, Open Educational Resources (OER) and the Creative Commons license (CC) are interesting solutions available to alleviate such problems of MOOC. This research presents a new framework that effectively connects OER and MOOC for a life-long e-Learning platform for Thai people. We utilize the Fedora Commons repository for an OER back-end, and develop a new front-end to manage OER resources. In addition, we introduce a “FedX API” - including a packet encapsulation and a data transmission module - that organizes educational resources between both systems. We also proposed the CC declaring function to help participants on-the-fly declare their content license; therefore, any resources must be granted as an open licensing. Another important function is a Central Authorized System (CAS) which is applied to develop single signing-on to facilitate the OER-MOOC connection. Since the framework is designed to support the massive demand, the concurrent access capability is also evaluated to measure the performance of the proposed framework. The results show that the proposed framework can provide up to 750 concurrencies without any defects. The FedX API does not produce bottleneck trouble on the interoperability framework in any cases. In addition, resources can be exchanged among the third-party OER repositories by an OAI-PMH harvesting tool.

This paper presents a new dimension in e-learning by collecting and analyzing physiological data during real-world e-learning sessions. Two different content materials, namely Interactive (IM) and Non-interactive (N-IM), were utilized to determine the physiological state of e-learners. Electrocardiogram (ECG) and Skin Conductance Level (SCL) were recorded continuously while learners experienced IM and N-IM for about 25 minutes each. Data from 18 students were collected for analysis. As a result significant difference between IM and N-IM was observed in SCL (p <.01) meanwhile there were no significance in other indices such as heart rate and its variability, and skin conductance response (SCR). This study suggests a new path in understanding e-learners' physiological state with regard to different e-learning materials; the results of this study suggest a clear distinction in physiological states in the context of different learning materials.

In our previous project, an XML-based authoring tool was provided for teachers to script multimedia teaching material with animated agents, and a stand-alone learning system was designed for students to display the material and interact with animated agents. We also provided evidence that the authoring tool and learning system in computer-assisted learning systems successfully enhances learning performance. The aim of this study is to continue the previous project, to develop a Web-based multimedia learning system that presents materials and an animated agent on a Web browser. The Web-based multimedia learning system can provide an opportunity for students to engage in independent learning or review of school course work. In order to demonstrate the efficiency of this learning system, it was applied to one elementary school. An experimental material, `Road Traffic Safety', was presented in two learning systems: a Web-based PowerPoint learning system and a Web-based multimedia learning system. The experiment was carried out in two classes that had a total of thirty-one 3rd-grade students. The results suggest that using our authoring tool in a Web-based learning system can improve learning, and in particular, enhance learners' problem-solving ability. Students with higher achievement on the post-test showed better comprehension in problem-solving questions. Furthermore, the feedback from the questionnaire surveys show students' learning interest can be fostered when an animated agent is integrated into multimedia teaching materials, and that students prefer to adopt the Web-based multimedia learning system for independent learning after school.

In this paper we present our investigation into improving the performance of our computer-assisted language learning (CALL) system through exploiting the acoustic model and features within the speech recognition framework. First, to alleviate channel distortion, speaker-dependent cepstrum mean normalization (CMN) is adopted and the average correlation coefficient (average CC) between machine and expert scores is improved from 78.00% to 84.14%. Second, heteroscedastic linear discriminant analysis (HLDA) is adopted to enhance the discriminability of the acoustic model, which successfully increases the average CC from 84.14% to 84.62%. Additionally, HLDA causes the scoring accuracy to be more stable at various pronunciation proficiency levels, and thus leads to an increase in the speaker correct-rank rate from 85.59% to 90.99%. Finally, we use maximum a posteriori (MAP) estimation to tune the acoustic model to fit strongly accented test speech. As a result, the average CC is improved from 84.62% to 86.57%. These three novel techniques improve the accuracy of evaluating pronunciation quality.

In medical education, many of computerized Problem-Based Learning (PBL) systems are used into their training curricula. But these systems do not truly reflect the situations which practitioners may actually encounter in a real medical environment, and hence their effectiveness as learning tools is somewhat limited. Therefore, the present study analyzes the computerized PBL teaching case, and considers how a clinical teaching case can best be presented to the student. Specifically, this paper attempts to develop a web-based PBL system which emulates the real clinical situation by introducing the concept of a "time sequence" within each teaching case. The proposed system has been installed in the medical center of National Cheng Kung University in Taiwan for testing purposes. The participants in this study were 50 of 5th grade (equivalent to 1st grade students in a medical school of the American medical education system) students for the evaluation process. Some experiments are conducted to verify the advantages of designing teaching cases with the concept of the "time sequence."

We have developed a visualization system for dialog text exchanged in e-learning virtual classrooms. In this system, text-based online discussions among learners are effectively visualized as discussions held in a virtual classroom in cyberspace. Discussion participants are displayed as avatars. The virtual classroom maintains the interest of learners because it incorporates professional camerawork and switching know-how based on rules derived from an analysis of 42 TV programs. The gestures of the CG avatar depend on the dialog text. A series of virtual classroom experiments confirmed that elementary and junior high school students maintained an interest in using the system.

A combination of slides used in a presentation and a video recording of the circumstances of the presentation are quite useful for many applications, such as e-learning. However, to create new content from these with current authoring tools requires considerable effort for the author and the products have reduced flexibility. In this paper, we propose the preparation of a unifying function without creating new content manually. We also propose a new approach to search unified presentation manuscripts for slides matched with given keywords by considering the features peculiar to the presentation slides. We propose impression indicators to express how well a slide matches the given keywords. We also propose a system for retrieving a sequence of desired presentation slides from archives of the combined slides and video. We named the system Unified Presentation Slide Retrieval by Impression Search Engine or UPRISE. We describe the system configuration of UPRISE and the experimentation undertaken to evaluate the effect of the proposed indicators and to compare the results with those of the traditional tf.idf retrieval method.