PENGELASAN SOALAN PEPERIKSAAN BERLANDASKAN TAKSONOMI BLOOM MENGGUNAKAN PEMBELAJARAN MESIN. (Malayalam)

An examination is needed to evaluate how much the students understand what is being thought. In order to have a quality and effective examination, instructors should have a guideline on how to develop quality questions from different cognitive levels in a balanced way that can evaluate the students. Thus, many instructors today use Bloom's Taxonomy which is a framework that developed to assess students' intellectual abilities and skills. However, it is quite challenging for the examiner to classify out the question based on Bloom's Taxonomy manually. Hence, this study aims to propose a question classification model to classify question based on Bloom's Taxonomy cognitive domain by using machine learning approach. The classifiers used are Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF) and K-Nearest Neighbour (KNN). To obtain a more accurate result, the dataset collected will undergo pre-process text and feature extraction such as bag-of-words. A user-friendly website was developed to facilitate instructors to classify their exam questions easily and quickly including viewing the analysis results from the classifier. The prototype from this study can help instructors to analyze exam questions to meet the needs for different cognitive levels for students according to the level of study. [ABSTRACT FROM AUTHOR]

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