Tessai Hayama

Qualitative, Quantitative Evaluation of Ideas in Brain Writing Groupware

The problem with traditional Brain Writing (BW) is that the users are restricted from viewing all sets of ideas at one time; and they are also restricted from writing down more than three ideas at a time. In this research we describe distributed experimental environment for BW which was designed to obtain better results and can thus eliminate the problems of traditional BW technique. The actual experimental system is an integration of three BW modes with mutually different features and characters. We conducted three different tests implementing this environment, and confirmed quality and quantity of ideas generated by three different groups. It was confirmed that unrestricted inputs are effective in generating a large quantity of ideas, whereas limiting the number of sharable/viewable ideas shows better tendency in some aspects. However, qualitative evaluation results were not confirmed as different functions show variant results. The evaluation of the functions that support viewing and sharing of ideas show that synergy is not always an advantage in generating ideas. The results of number of ideas in correlation with time show that 20 minutes time was appropriate to conduct BW in distributed environment.

Detecting TV Program Highlight Scenes Using Twitter Data Classified by Twitter User Behavior

This paper presents a novel TV event detection method for automatically generating TV program digests by using Twitter data. Previous studies of TV program digest generation based on Twitter data have developed TV event detection methods that analyze the frequency time series of tweets that users made while watching a given TV program; however, in most of the previous studies, differences in how Twitter is used, e.g., sharing information versus conversing, have not been taken into consideration. Since these different types of Twitter data are lumped together into one category, it is difficult to detect highlight scenes of TV programs and correctly extract their content from the Twitter data. Therefore, this paper presents a highlight scene detection method to automatically generate TV program digests for TV programs based on Twitter data classified by Twitter user behavior. To confirm the effectiveness of the proposed method, experiments using a TV soccer program were conducted.

Investigation of a team-based activity support system used in a Project-based learning course

In recent years, student-driven learning has been used to improve education in schools. Project-based learning (PBL) is often adopted to facilitate student-driven learning. In PBL, it is very important for the learners to have successful experiences when participating in team activities. However, every team activity in a PBL class is not always successful because learners with poor performance, caused by factors such as low motivation and weak problem-solving and communication skills, often participate in the class. With this in mind, our goal is to develop a system to support team-based activity within a PBL course using information technology. As the starting point of the research, we investigate how to use a basic team-based activity support system in a PBL course.

Face-to-Face Collaborative Learning by Enhancing Viewpoint-Sharing of Learning Materials

In face-to-face collaborative learning, learners develop an argument about a common learning theme in a small group to understand the subject more deeply. Forming a more convincing argument often involves individual study, in which the learners study additional materials about the theme prior to the collaborative learning session. However, it is difficult for learners without sufficient argumentative skills to incorporate learned knowledge, which is acquired in individual study, into an argument. Therefore, we developed a system that supports face-to-face collaborative learning by enhancing viewpoint-sharing of learning materials. The proposed system provides visual representations of different viewpoints of each learning material for the learners while they discuss the theme. In our experiment, we confirmed the usefulness of the proposed system by comparing it with collaborative learning sessions without a system.