This study presents the development and evaluation of a critical paper reading support system named "Forest/CR." The system aims to enhance learners' critical reading skills by facilitating the formation of reading strategies. It achieves this by recognizing questions that learners were unable to generate independently and by allowing them to compare their interpretations with those of others. The system features functions such as paper content organization, comparison of reading content, and comprehensive evaluation support. An experimental study involving third-year undergraduate students demonstrated that the system effectively broadened students' reading activities, promoted the generation of critical questions, and supported the learning of new reading strategies.

In this study, we adopt the Thinking Like a Historian Approach (TLH), which systematizes the historical inquiry process of historians as a learning model. By expanding Linked Open Data (LOD) using a large language model (LLM) based on an ontology that defines TLH concepts, we construct a white-box-oriented learning resource that ensures comprehensiveness, reproducibility, learning controllability, and maintainability. The historical learning support system developed based on this learning resource has adaptive question generation function and historical information visualization function in an open-ended historical inquiry space.

Research meetings are critical forums for sharing and evolving ideas, where the depth of the discussion can significantly enhance the research content. Effective meetings require not just a recapitulation of the discussions but a thorough examination of the thoughts and decisions preceding the meeting. In this paper, we propose a support system that promotes metacognitive reflection by aligning pre-meeting preparations with post-discussion feedback, thus enhancing the overall research process and outcomes.

Language learning resources, such as dialogue scenarios, are important for supporting second language (L2) learning by engaging learners in simulated conversations or role-plays. However, there are often barriers to the wider dissemination of these resources, including the lack of a standard format for distributing them, the difficulties of maintaining and updating them over time, and the need for effective means of tracking their use and impact. This paper presents an integrated framework for fostering the wider dissemination of L2 learning resources, with a focus on dialogue scenarios. The framework is designed to address the above challenges by providing a novel approach for creating and distributing dialogue scenarios, as well as a set of guidelines to ensure that they are pedagogically sound and compatible with learning management systems (LMS). The paper also discusses the potential benefits of the framework for L2 learners and educators and outlines future directions for the project.

To form and maintain smooth interpersonal relationships in various situations in social life, the ability to appropriately understand the mental states of others, such as their intentions and feelings, is required. However, it is difficult to infer the unobservable mental states of others, and it is also difficult to learn from others how to perform inferring activities that are not externalized. In this study, we focus on “dramatic scripts” as a learning material that contributes to the training of the ability to understand others and propose a learning support system that aims to improve the ability to understand others and to cultivate an attitude of understanding others through inference activities about the emotions and conflicts of characters.

In this research, we focused on research execution activities as a field of ill-defined problem solving and developed a mechanism to grasp learners’ thought processes based on interpretation rules and a generic ontology of research activities. The idea behind this mechanism is to prompt learners’ awareness of their own thinking processes so as to foster their metacognitive skills. In this paper, we discuss the features of a support system that we developed to achieve this goal.

Although writing activities present a good opportunity to develop metacognitive skills, it is difficult for novice learners to be aware of logical inconsistencies or a lack of perspective, even in their own documents. To promote such learners’ metacognitive activities, a novel eye-movement visualization system is proposed that activates learners’ metacognitive inference activities by expanding a previously proposed framework. The system intuitively visualizes all reading processes of the expert, who critically reads the learner’s document, as a timeline graph. In addition, it has several support functions that provide information on the expert’s characteristic reading behaviors and the logical structure of documents. The initial experiment showed that the proposed graph visualization system has the potential to activate learners’ metacognitive inference activities, even after verifying the previously proposed eye-movement visualization tools. It was also confirmed that learners can generate the correction points by themselves, even in documents that they had already checked critically before submission.

When holding creative discussions with other people, it is important not only that the speaker accurately understand what he/she wants to convey but also that the speaker express knowledge in a way that is understandable to the listener. “Reflective thinking” (knowledge restructuring) in which one considers what one wants to say and “verbalization of reasoning” driven by thinking that is conscious of other people are mutually interactive and deepen understanding of what one is trying to say. This study focuses on academic research meetings as a forum conducive to creative discussions and on the preparation of research materials (research documentation) as the product of the verbalization of reasoning that takes knowledge transmission into account. It proposes, in particular, a self-conversation support system that supports the iteration of these two activities.

It is difficult to capture a person’s thinking processes because of the invisible, shapeless, complex structure of thought. In order to approach implicit thinking processes, we propose a framework for capturing a portion of meta-level thinking processes from gaze behaviors. Here, we focus on the meta-level thinking processes in the dissolution of belief conflicts, and introduce the concept of semi-metacognitive-level thinking, which allows us to make assumptions about the monitoring/control cycle based on gaze behaviors toward externalization objects of base-level thinking. In this paper, we discuss a framework for capturing metacognitive thinking processes from gaze behaviors and propose an ontology to embody the concept of the framework.

The purpose of this research is to support learners in self-directed learning on the Internet using automatically generated support using the current state of the semantic web. The main issue of creating meaningful content-dependent questions automatically is that it requires the machine to understand the concepts in the learning domain. The originality of this work is that it uses Linked Open Data (LOD) to enable meaningful content-dependent support in open learning space. Learners are supported by a learning environment, the Semantically Enhanced Open Learning Space (SOLS). Learners use the system to build a concept map representing their knowledge. SOLS support learners following the principle of inquiry-based learning. Learners that request help are provided with automatically generated questions that give them learning objectives. To verify whether the current system can support learners with fully automatically generated support, we evaluated the system. The results showed that LOD based support was feasible. Learners felt that the support provided was useful and helped them learn. The question support succeeded in improving the development of learners’ historical considerations and deep historical thinking skills. In addition, the engagement and interest in history of learners was improved by the questions. The results are meaningful because they show that LOD based support can be a viable tool to support learners in open learning space and that the question support has potential to support learners during a long time study.

Historical events include lessons of good and bad behaviors of human beings. To discover these lessons, we must generalize the historical event by the basic attributes, so that we can notice the underlying patterns that commonly occur in multiple historical events. Based on this viewpoint, we have developed a learning system that supports the generalization of historical events based on the basic attributes and helps finding lessons by comparing common patterns from two generalized events. In this paper, the effect of our system is discussed based on the experimental result.

We propose an embodied conversational agent based on the willingness to communicate (WTC) model in L2 to help increase WTC in the context of English as a Foreign Language (EFL) by providing users with various daily conversation contexts. To simulate realistic and efficient conversations, we adopted a semantic approach in the response generation and created a system with flexible and adaptable domain knowledge, user's intent detection, and mixed-initiative conversation strategy. Our evaluation of the proposed system demonstrated its potential to increase WTC in the EFL context.

Historical thinking is the type of thinking that learns lessons from past historical events and applies them to the modern world. In order to apply lessons, the future situation after the lessons have been applied should be inferred. The future situation has been affected by the causal relationships between people and between properties of the people. In this study, an “if thinking” learning method is introduced so as to understand such causal relationships. In this approach, the change of one historical element is given and its influence on other elements is asked as a question. To answer the question, consider various causal relationships between people and their properties must be considered. In this study, we have also developed a system for presenting “if” situations and judging learners’ answers automatically. The experimental result showed that our system was effective in acquiring causal relationships.

This research’s objective is to train learners’ meta-cognitive skills in self-directed learning of history. Learners use a learning environment that provides support and regulates their learning processes using inquiry-based learning. Learners use the system to learn in an open learning space reinforced with semantic information. All the provided support is generated automatically using this semantic open learning space. The support is provided in the form of inquiry questions generated by the system. These questions are designed to direct learners to new information and trigger history thinking.

Design pattern is a description of the object-oriented design that gives high-quality and re-usable solutions for frequently occurring problems. A design pattern is formed based on the various experiences of predecessors who made ineffective designs. If students can grasp the design policy of predecessors from the design pattern, they can create good designs and apply them to new problems. The objective of this research is to propose a learning method for understanding design policy of the design pattern and to construct a meta-learning system for proposed learning method.

In this research, we implemented a system for measuring schema formulation on mobile terminals, making it easy to use in ordinary classrooms. We operated this system in an elementary school over an extended period of one month. The results showed that operating the system (1) was useful for schema formulation in students, particularly if they were weak in arithmetic, and (2) helped teachers understand students’ level of comprehension, suggesting that it could help in teaching according to level of comprehension. This article gives an overview of the system developed and reports on the results of operation in the elementary school.

This paper describes a unique learning system that cultivates a positive attitude toward learning through presentation design activities. Learners are assigned two tasks: a declarative knowledge task, and a presentation task that involves selecting slides to create a presentation from among a set of slides that is provided. The slides have been prepared so that some of the slides are relevant and support the theme of the presentation while others are redundant or may even contain wrong information. A learner model is presented that captures the attitudes of subjects toward learning as they perform these tasks.

Data mining has emerged as a significant technology for discovering knowledge in vast quantities of data. It is however accompanied by the danger that private information will be revealed in the processing of data mining. Hence, privacy-preserving data mining has received a growing amount of attention in recent years. In this paper, we propose a method to extract global fuzzy rules from distributed data in a privacy-preserving manner. This method transfers only values necessary for the extraction process without collecting any data at one place and can obtain the global fuzzy rules at all places. Each data set can be characterized by comparing the local fuzzy rules for each distributed data to the global ones for all data. We illustrate a result for experiments using Wine data from UCI Machine Learning Repository.

This research aims to give learners more content-dependent scaffolding in the self-directed learning of history. Learners use a system to build a concept map containing a chronology. The system is able to generate content dependent support adapted to the learners. To enable this support, we built a semantic open learning space using a natural language online encyclopedia and semantic information using the open linked data. The support is provided by the automatically generated questions and documents. The learners request questions when they need and the system will generate the questions depending on the concept map of the learner. The generated questions aim to leads the learners to new knowledge deepening their understanding.

The Problem Oriented System (POS), SOAP, and Focus Charting are popular methods of recording nursing activities as a way to improve collaborative nursing practices. However, explicit training methods for what and how nurses should write these recording systems for their nursing have not been sufficiently established. In regard to such trainings, we consider that a tutor’s thinking process is more important than the actual thinking result (e.g., a comment document). Our approach is to express tutor’s intention of teaching as a map in order to develop an educational tool that can be used in trainings on what and how to think in recording their activities. This paper presents an overview of the reviewing tool we developed, which provides tags as criteria of a tutor’s judgment. We have confirmed that, at least, the proposed method does not hinder a tutor’s review of a nurse’s case writing.

As described in this paper, we propose a slide-selection approach to overcome the problem of realizing a learning environment in which learners can construct presentations by choosing prepared slides. The advantage of this approach from the viewpoint of generating content-dependent guidance messages is that the system can extract the contents of each slide by adding tags that have meanings specified by an ontology, which contributes to enhancement of ontology-based intelligent meta-learning support.

Design patterns are good designs in object-oriented programming and are generated experientially by predecessors. We propose a learning method of understanding design patterns by transforming a program with a design pattern into that without one (alternative solution). We also develop a support system that encourages learners of generating appropriate alternative solution. Experimental results proved that the proposed method was effective for a deep understanding of design patterns.

We aim at developing a semester course to train thinking skills for 1st year bachelor students who just entered a university. Our goal in the educational program is to let learners perceive the isomorphism between thinking process for self-dialogue and one for discussion, and motivate them to learn the logical structure of self-dialogue through workshop and to acquire meta-cognition abilities through leading the discussion. We give knowledge co-creation task with a case that requires them to perform their meta-cognitive activities. In this paper, we will discuss the theoretical foundation and the design intention of a thinking skill development program. Then, we will report the implementation of the semester program and interesting analysis of the data collected.

Our research goal is to develop a system that can support learners in elementary education to acquire self-regulatory skills. Learners can enhance their learning motivation and build a foundation for life-long learning by developing self-regulatory skills as early as possible in elementary education. However, it is not easy for them to acquire such skills. In our research, we analyze factors of difficulties in displaying self-regulatory skills and clarify support functions to reduce these difficulties. Then we develop a support system to encourage learners’ continued self-regulated learning.

As described in this paper, we propose a presentation-based meta-learning scheme. First, we introduce the design rationale of our meta-learning scheme by introducing our conceptualization, which clarifies the kind of support to facilitate meta-learning that is embedded into our learning scheme. Secondly, we present support functions that we embed into the system. Thirdly, we conduct experiments to verify the meaningfulness of our learning scheme, which suggests the system can stimulate learners’ reflection on their learning processes. Furthermore, it can stimulate learners’ meta-learning communications. Results show that users tightened their criteria to evaluate their own learning processes and understanding states. It is useful for learners to facilitate change in their learning processes.

We have various kinds of time series such as stock prices. We understand them via their linguistic expressions in a natural language rather than conventional stochastic models. We propose an improved method to have a linguistic expression with a global trend and local features of time series. A global trend is extracted via aggregated values on the fuzzy intervals in the temporal axis and local features are specified as the positions of locally large differences between the original data and the data representing the global trend. We apply the method to the data of multimodal summarization for trend information (MuST).

To support Japanese teachers with sharing their knowledge on digital contents usage, this paper proposes a social networking service. While the digital contents prepared by Japan Science and Technology Agency are attractive for innovating education, most teachers spend much time to prepare their classes. We have found a necessity to define a maturity level for teachers on sharing knowledge. This paper proposes not only a user interface with a recommended message by maturity level but also an expanded data model for implementation. This proposal is expected to save time for preparing ICT-based classes and to improve classes by sharing knowledge.

When we generate knowledge, we initially have no knowledge and acquire it by observing data one by one. We memorize the raw data when the number of observed data is small and generate general knowledge when it becomes large. To simulate this learning process, we proposed a learning model with switching several knowledge representation and reasoning methods. In this model, the time when to switch is decided with the fixed rules. These rules are considered to be meta-knowledge because they control the learning process. In this paper, we propose a method acquiring the meta-knowledge for deciding the time of switching knowledge representation or reasoning method. For learning of the meta-knowledge, the correct answers can not to be given but just the evaluation of the learning process. We use Q-learning, therefore, a method of reinforcement learning. In the simulation, we apply the method to the iris plant data to acquire the meta-knowledge. The system with the acquired meta-knowledge has smaller number of rules than the old method for the similar rate correctly classified.

When we solve a problem, we initially have no knowledge and we memorize the raw data with observing data. Finally we have general knowledge for solving the problem. To simulate this learning process, we proposed a learning method with switching different levels of knowledge representations, reconstructing knowledge and switching reasoning methods. In the system, all given data are stored to generate new knowledge, but it is different from the one of our human's knowledge acquisition, in which we just memorize a limit number of data. Therefore, we limit it and when the number of stored data exceeds specified size, the system throws away the oldest data. In the simulation, we apply the method to the data set whose classes are changed periodically, and get a better result than the old method.

An autonomous agent had a ranged view of the absolute coordinate system, where it can receive accurate information in a range but noting out of the range. This is a considerably artificial situation. In this paper, we propose a staged view in distance and direction of the relative coordinate system, where an agent receives accurate information in neighborhood but only rough information in short and middle-distance areas. It reflects a human's view that we can see easily an object in the neighborhood but more difficult as distance becomes larger and we can see easily an object in the center direction but more difficult in the righter and lefter directions. We show by a numerical experiment for the pursuit problem, a multi-agent's benchmark problem, that the agent with the staged view learns effectively using Q-learning.

When we solve a problem, we firstly have no knowledge and gradually acquire some piece of knowledge by observing new data, and at last arrive at complete knowledge for solving the problem. We have a simple form of specific knowledge in the first stage and a complex form of a general one in the final stage. To simulate this kind of learning mechanism, we must combine several kinds of learning methods in several stages. We proposed a method of not only reconstructing rules and switching reasoning methods in each knowledge representation but also switching rule generation methods in several knowledge representation. We simulated the method by applying to the iris classification problem.

Novice learners in a problem solving domain build up their own understanding of its target area and make plans to solve a problem based on their understanding. Our goal is to develop a system for supporting those processes and the reuse, sharing and inheritance of expertise acquired by suffering troubles. Ontology is a key technique to achieve this goal. In this paper, we mainly focus on supporting learners' planning processes and describe our modeling framework for effective and efficient planning of problem solving workflow that includes learning processes.