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Studying the Effects of Performing Text Mining to Improve Classification of Clustered Questions based on Bloom Taxonomy
This project is about analyzing the effects of classifying the written exam question into cognitive level of Bloom’s taxonomy. Correctly analyze and classify the written exam questions into correct cognitive level can generate a good set of exam questions. As known by many educators, classifying exam question into its cognitive level is a tedious task and required full attention by educators. Moreover, there are situations where one keyword of cognitive level belongs to more than one level which could be an issue of difficult to determine the correct cognitive level of questions. To solve the problem of classifying exam question faces by educators, the techniques information retrieval of text mining were implements in this project. Before that, question bank are required to perform text preprocessing to generate the clean data. The activities done under text preprocessed are such as data transformation, tokenization of question and stop word removal. The effects of classifying the clustered data being analyzed to study the possible hidden pattern of classifying based on Bloom’s Taxonomy.
Classification, Clustering, Text-Mining.
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