FTFPOS-IDF: A Fuzzy Rule-Based Thematic Term Weighting Scheme for Bloom's Taxonomy Question Classification

The increasing adoption of Artificial Intelligence (AI) in education has created a growing demand for automated and reliable assessment systems. Existing Bloom’s Taxonomy (BT) question classification approaches commonly rely on TF-IDF-based weighting schemes, which assign static term weights and often fail to capture the varying thematic importance of terms across cognitive levels. To address this limitation, this study proposes a novel Fuzzy Thematic Feature and Part-of-Speech Inverse Document Frequency (FTFPOS-IDF) weighting scheme that integrates fuzzy rule-based reasoning with Natural Language Processing (NLP) to dynamically assign thematic weights according to Bloom’s Taxonomy relevance. The proposed framework combines Machine Learning (ML) and Deep Learning (DL) classifiers with Chi-Square feature selection to reduce irrelevant features and improve classification performance. Experimental results demonstrate that FTFPOS-IDF consistently outperforms conventional TF-IDF variants across multiple classification models. The highest performance was achieved by the Multilayer Perceptron (MLP) classifier with an accuracy of 86.7%. These findings indicate that fuzzy rule-based thematic weighting can effectively enhance Bloom’s Taxonomy question classification and support scalable, reliable, and sustainable digital assessment systems in educational environments.