Conceptual data modeling is an important activity in database design. However, it is difficult for novice learners to master its skills. In the conceptual data modeling, learners are required to detect and correct errors of their artifacts by themselves because modeling tools do not assist these activities. We call such activities self checking, which is also an important process. However, the previous research did not focus on it and/or the data collection of self checks. The data collection of self checks is difficult because self checking is an internal activity and self checks are not usually expressed. Therefore, we developed a method to help learners express their self checks by reflecting on their artifact making processes. In addition, we developed a system, KIfU3, which implements this method. We conducted an evaluation experiment and showed the effectiveness of the method. From the experimental results, we found out that (1) the novice learners conduct self checks during their conceptual data modeling tasks; (2) it is difficult for them to detect errors in their artifacts; (3) they cannot necessarily correct the errors even if they could identify them; and (4) there is no relationship between the numbers of self checks by the learners and the quality of their artifacts.

In this paper we describe a mixed reality-supported interactive viewing enhancement museum display system: Visual-dimension Interact System (VIS). With a transparent interactive interface, the museum visitor is able to see, manipulate, and interact with the physical exhibit and its virtual information, which are overlapped on one other. Furthermore, this system provides the possibility for visitor to experience the creation process in an environment as close as possible to the real process. This has the function of assisting the viewer in understanding the exhibit and most importantly, gaining a so-to-speak hands-on experience of the creation process itself leading to a deeper understanding of it.

Software agents are knowledgeable, autonomous, situated and interactive software entities. Agents' interactions are of special importance when a group of agents interact with each other to solve a problem that is beyond the capability and knowledge of each individual. Efficiency, performance and overall quality of the multi-agent applications depend mainly on how the agents interact with each other effectively. In this paper, we suggest an agent model by which we can clearly distinguish different agent's interaction scenarios. The model has five attributes: goal, control, interface, identity and knowledge base. Using the model, we analyze and describe possible scenarios; devise the appropriate reasoning and decision making techniques for each scenario; and build a library of reasoning and decision making modules that can be used readily in the design and implementation of multiagent systems.