Automatic Generation of Teachers’ Course Preferences Using Document Clustering

Abstract

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The current study examined the automated course preferences of teachers using document clustering. Data regarding teachers’ course preferences and course outlines were collected and preprocessed for further analysis. Two separate clustering solutions were generated for teachers and courses datasets. The clustering solution for teachers contained clusters of similar faculty members grouped together on the basis of their course preferences and courses taught by them in previous years. The clustering solution generated for courses contained the list of course outlines of assigned courses. Good quality clusters for both teachers and courses were generated using K-means clustering method in CLUTO software package. The generated clustering solutions were mapped for automated generation of course preferences for each teacher in the dataset. Precision, Recall and F-measure values were also reported and they indicated promising results.