Self-review is essential to improving presentation, particularly for novice/unskilled researchers. In general, they could record a video of their presentation, and then check it out for self-review. However, they would be quite uncomfortable due to their appearance and voice in the video. They also struggle with in-depth self-review. To address these issues, we designed a presentation avatar that reproduces presentation made by researchers. The presentation avatar intends to increase self-awareness through self-reviewing. We also designed a checklist to aid in a detailed self-review, which includes points to be reviewed. This paper also demonstrates presentation avatar systems that use a virtual character and a robot, to allow novice/unskilled researchers as learners to self-review their own presentation using the checklist. The results of case studies with the systems indicate that the presentation avatar systems have the potential to promote self-review. In particular, we found that robot avatar promoted engagement in self-reviewing presentation.

Investigative reports plagiarized from the web should be eliminated because such reports result in ineffective knowledge construction. In this study, we developed an investigative report writing support system for effective knowledge construction from the web. The proposed system attempts to prevent plagiarism by restricting copying and pasting information from web pages. With this system, students can verify information through web browsing, externalize their constructed knowledge as notes for report materials, write reports using these notes, and remove inadequacies in the report by reflection. A comparative experiment showed that the proposed system can potentially prevent web page plagiarism and make knowledge construction from the web more effective compared to a conventional report writing environment.

This paper describes an indexing framework for adaptive arrangement of mechanics problems in ITS (Intelligent Tutoring System). There have been some studies for adaptive arrangement of problems in ITS. However, they only choose a solution method in order to characterize a problem used in the practice. Because their target domains have been sufficiently formalized, this kind of characterization has sufficed to describe the relations between any two problems of such a class. In other words, here, it is enough to make students understand only the solution methods for the given class of problems. However, in other domains, it is also important to understand concepts used in the problems and not only to understand solution methods. In mechanics problems, concepts such as mechanical objects, their attributes, and phenomena composed of the objects and the attributes also need to be taught. Therefore, the difference between solution methods applied is not sufficient to describe the difference between two given problems. To use this type of problems properly in the practice, it is necessary to propose an advanced new characterization framework. In this paper, we describe a mechanics problem with three components: (1) surface structure, (2) phenomenon structure, (3) solution structure. Surface structure describes surface features of a problem with mechanical objects, their configuration, and each object's attributes given or required in the problem. Phenomenon structure is described by attributes and operational relations among them included in the phenomenon specific to the surface structure. Solution structure is described by a sequence of operational relations which compute required attributes from given attributes. We call this characterizing indexing because we use it as index of each problem. This paper also describes an application of the indexing to arrangement of problems. We propose two mechanisms of control: (a) reordering of a problem sequence, and (b) simplifying of a problem. By now, we have implemented basic functions to realize the mechanisms except for the part of interface.

This paper discusses the design of an ITS to realize a load-oriented tutoring to enhance the student's explanation understanding. In the explanation understanding, it is to be hoped that a student not only memorizes the new information from an explanation, but also relates the acquired information with his/her own knowledge to recognize what it means. This relating process can be viewed as the one in which the student structures his/her knowledge with the explanation. In our ITS, we regard the knowledge-structuring activities as the explanation understanding. In this paper, we propose an explanation, called a load-oriented explanation, with the intention of applying a load to the student's knowledge-structuring activities purposefully. If the proper load is applied, the explanation can induce the student to think by himself/herself. Therefore he/she will have a chance of gaining the deeper understanding. The important point toward the load-oriented explanation generation is to control the load heaviness appropriately, which a student will bear in understanding the explanation. This requires to estimate how an explanation promotes the understanding activities and how much the load is applied to the activities. In order to provide ITS with the estimation, we have built an Explanation Effect Model, EEM for short. Our ITS consists of an explanation planner and a self-explanation environment. The planner generates the load-oriented explanation based on EEM. The system also makes a student explain the explanation understanding process to himself/herself. Such self-explanation is useful to let the student be conscious of the necessity of structuring his/her knowledge with the explanation. The self-explanation environment supports the student's self-explanation. Furthermore, if the student reaches an impasse in self-explaining, the planner can generate the supporting explanation for the impasse.