Tomoko Kojiri

Automatic Question Generation for Educational Applications – The State of Art

Recently, researchers from multiple disciplines have been showing their common interest in automatic question generation for educational purposes. In this paper, we review the state of the art of approaches to developing educational applications of question generation. We conclude that although a great variety of techniques on automatic question generation exists, just a small amount of educational systems exploiting question generation has been developed and deployed in real classroom settings. We also propose research directions for deploying the question technology in computer-supported educational systems.

User-oriented interface for collaborative learning environment

In the collaborative learning environment, there are various kinds of information, such as faces of students and learning materials, and the interface which shows these information effectively is required. If the information is provided effectively, students would communicate with others smoothly and progress the learning efficiently. When we study in the group, we tend to focus on what we are interested in or what we thought to be important in order to get meaningful information efficiently. Therefore, our objective is to construct a user-oriented interface which reflects preferences or characteristics of individual students and only provides meaningful information for students. In this paper, the authors propose factors that are needed as the interface of collaborative learning in order to support effective and smooth learning. Then, they introduce the example of user-oriented interface in which students are able to focus on a particular student in the group during the discussion.

Focus support interface based on actions for collaborative learning

With the development of information and communication technologies, learners can easily study with others in the distributed environment. However, it is still hard for them to share interaction with others efficiently because of the limited communication means. In order for learners to study collaboratively with others and immerse in learning, it is important for them to grasp directly the actions occurred in the learning environment, such as making utterances, facing to other learners, writing memos, etc. Moreover, they should observe the collaborative learning environment appropriately according to their focusing intentions. In this paper, we analyze the activities occurred in the collaborative learning environment, and propose a method for detecting focusing intention of the learner. Then, we address the effective view change based on the focusing intention in the collaborative learning environment. From our experimental result, our method of focusing intention could detect 70% of focusing targets of learners correctly.

Effective Presentation Speech Support System for Representing Emphasis-Intention

A research presentation integrates slides and speech. If these two aspects do not represent the same intention, the presentation will probably fail to effectively explain the presenter’s intention. This paper focuses on the representation of the critical contents in a presentation. In an effective speech, the speaker adds more intonation and stress to emphasize the importance of the slide contents. Audiences recognize that important contents are those that are explained in a stronger voice or that are said after a short pause. However, in ineffective speeches, such voice effects do not always correspond to the important contents that are indicated by slides. On slides, the important contents are represented by levels of text indentation and size, color, and animation. This research develops a presentation speech support system that estimates important contents from slides and voices that might be recognized by audiences and extracts numerical differences. In addition, the system provides comments and feedback to improve speeches.

Modeling understanding level of learner in collaborative learning using bayesian network

In the collaborative learning, the learner often focuses on the particular person based on the persons understanding level. If the information about particular person whom the learner focuses on is acquired automatically, the learner is able to understand the target person easily. So, it is necessary to estimate the understanding levels of others. However, in the collaborative learning environment, since the learner does not utter all the knowledge that he knows, to externalize explicitly the understanding levels of the learners is difficult. The understanding level about the knowledge which is not uttered by the learner is also estimated from the uttered information. So, in this paper, we define solution network which represents the relations of knowledge in the exercise with their strengths. When the utterance is generated, the understanding level of the uttered knowledge and its related knowledge is estimated by means of the solution network.

Lesson Discovery Support Based on Generalization of Historical Events

Historical events include lessons of good and bad behaviors of human beings that can be readily applied to the modern world. To discover these lessons, one must generalize the basic attributes of multiple historical events, so that one can perceive the underlying patterns that commonly occur. This paper proposes a novel scheme for uncovering the typical patterns that emerge from multiple historical events by generalizing historical characters. We then construct a learning system that supports the generalization and discovery of common patterns based on the proposed scheme.

Annotation interpretation of collaborative learning history for self-learning

In collaborative learning, learners exchange opinions through communication. When learners study the same type of exercises after the collaborative learning, they recall that communication, find effective utterances, and derive an answer by themselves. Therefore, in an collaborative learning support environment, it is useful for learners to monitor communication history after the learning has been finished. In the collaborative learning of exercises that contain an answer and answering paths, grasping answering path that learners derive during the learning and detecting effective utterances based on their answering paths are important. In our approach, the function to add annotations to utterances during learning is introduced in order to grasp effective utterances that students think during the learning. Then, based on the added annotations, learning situation of other learners and effective utterances for learners are derived.

Collaborative Learning Environment which Enforces Students to Induce Ideas

In order to realize the functionality or environment for collaboration on the information network, the following subjects must be systematically investigated: 1) to organize participating students as a collaborative group, 2) to support effective actions/reactions among students, 3) to not only coordinate the discussion activity successfully but also promote the interactions successively, 4) to enable every student to reach the final discussion goal and encourage the mutual understanding.

Verbalization Support for Motor Skill Using Form Drawing Tool Based on Skeleton Model

Abstract To acquire motor skill, it is important to understand ones own movement and externalize it in words. Movement is regarded as a sequence of forms. Therefore, the objective of this research is to develop a system for supporting externalization of forms that characterize movement. Since drawing is regarded as one of the effective tools for understanding the target movement, a drawing tool is developed in which body form can be drawn by manipulating a skeleton model. This tool also monitors the drawing time and points out the body parts that took a long time to draw but are not externalized. In this paper, the developed drawing tool is introduced and the effect of the tool for understanding body movement in baseball batting is investigated through experiment.

“If Thinking” Support System for Training Historical Thinking☆

Abstract 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.