In historical learning, to grasp the causal relationship between historical events and to understand factors that bring about important events are significant for fostering the historical thinking. However, some students are not able to find historical events that have causal relationships. The view of observing the historical events is different among individuals, so it is not appropriate to define the historical events that have causal relationships and impose students to remember them. The students need to understand the definition of the causal relationships and find the historical events that satisfy the definition according to their viewpoints. The objective of this paper is to develop a support system for understanding the meaning of a causal relationship and creating causal relation graphs that represent the causal relationships between historical events. When historical events have a causal relationship, a state change caused by one event becomes the cause of the other event. To consider these state changes is critically important to connect historical events. This paper proposes steps for considering causal relationships between historical events by arranging the state changes of historical people along with them. It also develops the system that supports students to create the causal relation graph according to the state changes. In our system, firstly, the interface for arranging state changes of historical people according to the historical events is given. Then, the interface for drawing the causal relation graph of historical events is provided in which state changes are automatically indicated on the created links in the causal relation graph. By observing the indicated state changes on the links, students are able to check by themselves whether their causal relation graphs correctly represent the causal relationships between historical events.

Though causal order of message delivery simplifies the design and development of distributed applications, the overhead of enforcing it is not negligible. We claim that a causal order algorithm which does not send any redundant information is efficient in the sense of communication overhead. We characterize and classify the redundant information into four categories: information regarding just delivered, already delivered, just replaced, and already replaced messages. We propose an efficient causal multicast algorithm which prevents propagation of these redundant information. Our algorithm sends less amount of control information needed to ensure causal order than other existing algorithms and can also be applied to systems whose communication channels are not FIFO. Since our algorithm's communication overhead increases relatively slowly as the number of processes increases, it shows good scalability feature. The potential of our algorithm is shown by simulation study.