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Grouping of Questions From a Question Bank Using Partition-Based Clustering
Abstract
During automatic test paper generation, it is necessary to detect percentage of similarity among questions and thereby avoid repetition of questions. In order to detect repeated questions, the authors have designed and implemented a similarity matrix-based grouping algorithm. Grouping algorithms are widely used in multidisciplinary fields such as data mining, image analysis, and bioinformatics. This chapter proposes the use of grouping strategy-based partition algorithm for clustering the questions in a question bank. It includes a new approach for computing the question similarity matrix and use of the matrix in clustering the questions. The grouping algorithm extracts n module-wise questions, compute n × n similarity matrix by performing n × (n-1)/2 pair-wise question vector comparisons, and uses the matrix in formulating question clusters. Grouping algorithm has been found efficient in reducing the best-case time complexity, O (n× (n-1)/2 log n) of hierarchical approach to O (n × (n-1)/2).
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