A method is proposed for resolving human congestion at a specific time at key spots in an area. Sensing data on real-world human flows are analyzed, and important information for changing movement behavior is accordingly provided. By using conventional approaches, this was a difficult task, whereas in the proposed approach, the targets and timing of providing information for congestion mitigation are determined based on spot importance. A congestion transition model is constructed from actual data and the results of a questionnaire survey. Finally, congestion mitigation in key spots is simulated after movement recommendation has been provided.

This study aimed to cluster learners based on the structures of the knowledge maps they created. Learners drew their own knowledge maps to reflect their learning activities. Our system collected individual knowledge maps from many learners and clustered them to generate an integrated version of the knowledge maps of each cluster. We applied the graph analysis method to extract important keywords from the knowledge map. The results of the analysis showed that the utilization of the knowledge map helped to improve lectures and grasp the learners' level of understanding. We conducted surveys asking course managers to evaluate the effectiveness of the integrated knowledge maps of learners included in the cluster and received both positive and negative responses.

No abstract

No abstract