The Journal of Machine Learning Research
Towards a syllabus repository for computer science courses
Proceedings of the 38th SIGCSE technical symposium on Computer science education
A framework for describing and comparing courses and curricula
Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education
Probabilistic latent semantic visualization: topic model for visualizing documents
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards automatic syllabi matching
ITiCSE '09 Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education
Analysis of computer science related curriculum on LDA and Isomap
Proceedings of the fifteenth annual conference on Innovation and technology in computer science education
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A good curriculum is crucial for a successful university education. When developing a curriculum, topics, such as economics, natural science, informatics, etc. are set first, and course syllabi are written accordingly. However, the topics actually covered by the course syllabi are not guaranteed to be identical to the initially set topics. To find out if the actual topics covered by the developed course syllabi, we developed a method of systematically analyzing course syllabi that uses latent Dirichlet allocation (LDA) and Isomap. In this paper, we propose the webbased curriculum analysis tool with this method, and demonstrate an example of the way the tool is used for analyzing computer science curricula.