Α Supervised Hybrid Weighting Scheme for Bloom's Taxonomy Questions using Category Space Density-based Weighting
Received: 27 January 2025 | Revised: 27 February 2025 | Accepted: 6 March 2025 | Online: 26 March 2025
Corresponding author: Didik Dwi Prasetya
Abstract
Question documents organized based on Bloom's taxonomy have different characteristics than typical text documents. Bloom's taxonomy is a framework that classifies learning objectives into six cognitive domains, each having distinct characteristics. In the cognitive domain, different keywords and levels are used to classify questions. Using existing category-based term weighting methods is less relevant because it is only based on word types and not on the main characteristics of Bloom's taxonomy. This study aimed to develop a more relevant term weighting method for Bloom's taxonomy by considering the term density in each category and the specific keywords in each domain. The proposed method, called Hybrid Inverse Bloom Space Density Frequency, is designed to capture the unique characteristics of Bloom's taxonomy. Experimental results show that the proposed method can be applied to all question datasets, considering term density in each category and keywords in each cognitive domain. Furthermore, the accuracy of the proposed method was superior on all datasets using machine learning model evaluation.
Keywords:
term, question, Bloom's taxonomy, space density, ; Machine LearningDownloads
References
M. Seaman, "Bloom’s taxonomy," in Curriculum and Teaching Dialogue, vol. 13, American Association for Teaching & Curriculum, 2012.
M. Chindukuri and S. Sivanesan, "Transfer learning for Bloom’s taxonomy-based question classification," Neural Computing and Applications, vol. 36, no. 31, pp. 19915–19937, Nov. 2024.
L. H. Waite, J. F. Zupec, D. H. Quinn, and C. Y. Poon, "Revised Bloom’s taxonomy as a mentoring framework for successful promotion," Currents in Pharmacy Teaching and Learning, vol. 12, no. 11, pp. 1379–1382, Nov. 2020.
P. Sahu, M. Cogswell, A. Divakaran, and S. Rutherford-Quach, "Comprehension Based Question Answering using Bloom`s Taxonomy," in Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), Dec. 2021, pp. 20–28.
A. S. Alammary, "Arabic Questions Classification Using Modified TF-IDF," IEEE Access, vol. 9, pp. 95109–95122, 2021.
M. Sudarma and J. Sulaksono, "Implementation of TF-IDF Algorithm to detect Human Eye Factors Affecting the Health Service System," INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi, vol. 4, no. 1, pp. 123–130, Feb. 2020.
M. Liang and T. Niu, "Research on Text Classification Techniques Based on Improved TF-IDF Algorithm and LSTM Inputs," Procedia Computer Science, vol. 208, pp. 460–470, Jan. 2022.
A. Aninditya, M. A. Hasibuan, and E. Sutoyo, "Text Mining Approach Using TF-IDF and Naive Bayes for Classification of Exam Questions Based on Cognitive Level of Bloom’s Taxonomy," in 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), Bali, Indonesia, Nov. 2019, pp. 112–117.
T. Zhang and S. S. Ge, "An Improved TF-IDF Algorithm Based on Class Discriminative Strength for Text Categorization on Desensitized Data," in Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence, Suzhou, China, Mar. 2019, pp. 39–44.
K. Chen, Z. Zhang, J. Long, and H. Zhang, "Turning from TF-IDF to TF-IGM for term weighting in text classification," Expert Systems with Applications, vol. 66, pp. 245–260, Dec. 2016.
Z. Tang, W. Li, and Y. Li, "An improved supervised term weighting scheme for text representation and classification," Expert Systems with Applications, vol. 189, Mar. 2022, Art. no. 115985.
M. O. Gani, R. K. Ayyasamy, A. Sangodiah, and Y. T. Fui, "USTW Vs. STW: A Comparative Analysis for Exam Question Classification based on Bloom’s Taxonomy," MENDEL, vol. 28, no. 2, pp. 25–40, Dec. 2022.
A. A. Yahya, Z. Toukal, and A. Osman, "Bloom’s Taxonomy–Based Classification for Item Bank Questions Using Support Vector Machines," in Modern Advances in Intelligent Systems and Tools, 2012, pp. 135–140.
M. Mohammed and N. Omar, "Question Classification Based on Bloom’s Taxonomy Using Enhanced TF-IDF," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 4–2, pp. 1679–1685, Sep. 2018.
M. Mohammed and N. Omar, "Question classification based on Bloom’s taxonomy cognitive domain using modified TF-IDF and word2vec," PLOS ONE, vol. 15, no. 3, 2020, Art. no. e0230442.
D. Sheelam, "Blooms data set." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/dineshsheelam/blooms-data-set.
P. Lu et al., "ScienceQA: Science Question Answering Dataset." https://scienceqa.github.io/#dataset.
Supriyono, A. P. Wibawa, Suyono, and F. Kurniawan, "Advancements in natural language processing: Implications, challenges, and future directions," Telematics and Informatics Reports, vol. 16, Dec. 2024, Art. no. 100173.
D. D. Prasetya, A. P. Wibawa, and T. Hirashima, "The performance of text similarity algorithms," International Journal of Advances in Intelligent Informatics, vol. 4, no. 1, pp. 63–69, Mar. 2018.
F. A. Ahda, A. P. Wibawa, D. D. Prasetya, and D. A. Sulistyo, "Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation," JOIV : International Journal on Informatics Visualization, vol. 8, no. 1, pp. 231–238, Mar. 2024.
S. Sucipto, D. D. Prasetya, and T. Widiyaningtyas, "A review questions classification based on Bloom taxonomy using a data mining approach," ITEGAM-JETIA, vol. 10, no. 48, pp. 161–170, Aug. 2024.
A. Alqahtani, H. Alhakami, T. Alsubait, and A. Baz, "A Survey of Text Matching Techniques," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6656–6661, Feb. 2021.
T. T. Goh, N. A. A. Jamaludin, H. Mohamed, M. N. Ismail, and H. Chua, "Semantic Similarity Analysis for Examination Questions Classification Using WordNet," Applied Sciences, vol. 13, no. 14, Jan. 2023, Art. no. 8323.
K. Jayakodi, M. Bandara, I. Perera, and D. Meedeniya, "WordNet and Cosine Similarity based Classifier of Exam Questions using Bloom’s Taxonomy," International Journal of Emerging Technologies in Learning (iJET), vol. 11, no. 04, Apr. 2016, Art. no. 142.
V. A. Silva, I. I. Bittencourt, and J. C. Maldonado, "Automatic Question Classifiers: A Systematic Review," IEEE Transactions on Learning Technologies, vol. 12, no. 4, pp. 485–502, Jul. 2019.
P. Sharma and N. Joshi, "Knowledge-Based Method for Word Sense Disambiguation by Using Hindi WordNet," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3985–3989, Apr. 2019.
T. Wang, Y. Cai, H. Leung, R. Y. K. Lau, H. Xie, and Q. Li, "On entropy-based term weighting schemes for text categorization," Knowledge and Information Systems, vol. 63, no. 9, pp. 2313–2346, Sep. 2021.
A. Sangodiah, T. J. San, Y. T. Fui, L. E. Heng, R. K. Ayyasamy, and N. A. Jalil, "Identifying Optimal Baseline Variant of Unsupervised Term Weighting in Question Classification Based on Bloom Taxonomy," MENDEL, vol. 28, no. 1, pp. 8–22, Jun. 2022.
D. E. Cahyani and I. Patasik, "Performance comparison of TF-IDF and Word2Vec models for emotion text classification," Bulletin of Electrical Engineering and Informatics, vol. 10, no. 5, pp. 2780–2788, Oct. 2021.
Downloads
How to Cite
License
Copyright (c) 2025 Sucipto, Didik Dwi Prasetya, Triyanna Widiyaningtyas
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
