Analysis of Bloom Taxonomy-Based Examination Data Using Data Mining - Kumar, Amit; Singh, Dinesh; Dhankhar , M. S. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Question classification means, the selection of a category of questions from a list of established question categories. It is a unique kind of text categorization in which there are considerable differences between the two forms, especially when the test questions comprise only a few terms that include or express the substance of the question. Consequently, creating exam questions is a stage that academics find very difficult. Thus, Bloom’s Taxonomy has become a framework for creating examinations that span a broad variety of cognitive levels based on the various abilities of students. Using data mining, this research proposes a strategy making use of Bloom’s Taxonomy to categorise exam questions cognitive levels. In this work, a CNN-model-based BiLSTM classifier is utilized to categorize questions using feature selection techniques. By employing Mutual Information feature selection, the BiLSTM classifier obtained the best classification model performance using the macro F1-measure. In conclusion, this study’s trials demonstrate that the feature selection approaches contributed positively to the performance of the classifier.