Bloom’s Taxonomy–Based Classification for Item Bank Questions Using Support Vector Machines
This paper investigates the effectiveness of support vector machines for the classification of item bank question into Bloom’s taxonomy cognitive levels. In doing so, a dataset of pre-classified questions has been collected. Each question has been processed through removal of punctuations, tokenization, stemming, term weighting, and length normalization. Using this dataset, the performance of support vector machines has been evaluated considering the effect of term frequency and stopwords removal. The results show a satisfactory performance of support vector machines, which declines as the frequency of the terms used to represent question increases. The best performance is obtained when term frequency is greater than or equal to two. Moreover, the results show that the removal of stopwords does not improve the performance significantly.
KeywordsSupport Vector Machine Term Frequency Concept Drift Item Question Question Classification
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