Improvement of Curriculum Analyzing System utilizing Syllabus-Term Co-clustering

Abstract

Comprehending the features of curricula provided by various higher education institutions is significant in designing and evaluating a curriculum. To facilitate comprehension of the curricula's features, Curriculum Analyzing System has been developed by Nozawa et al. utilizing document-clustering of syllabus data. However, speeding up and improvement of interactivity in the clustering procedure remained to be accomplished. In this article, a co-clustering method based on graph partitioning is introduced in order to improve the curriculum analyzing system. We show that recursive application of co-clustering to syllabus-term data makes the system more efficient and enables the users to understand the curricula's features more interactively.