Analysis of Bloom Taxonomy-Based Examination Data Using Data Mining
Keywords:
BiLSTM, CNN, Bloom’s taxonomy, Feature extraction, data miningAbstract
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.
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Dorça, Fabiano A., Luciano V. Lima, Márcia A. Fernandes, and Carlos R. Lopes. "Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: An experimental analysis." Expert Systems with Applications 40, no. 6 (2013): 2092-2101.
Abduljabbar, Dhuha Abdulhadi, and Nazlia Omar. "Exam questions classification based on Bloom's taxonomy cognitive level using classifiers combination." Journal of Theoretical and Applied Information Technology 78, no. 3 (2015): 447.
Hoque, M. Enamul. "Three domains of learning: Cognitive, affective and psychomotor." The Journal of EFL Education and Research 2, no. 2 (2016): 45-52.
Munir, Muhammad Tajammal, Saeid Baroutian, Brent R. Young, and Susan Carter. "Flipped classroom with cooperative learning as a cornerstone." Education for chemical engineers 23 (2018): 25-33.
Saido, Gulistan Mohammed, Saedah Siraj, Abu Bakar Bin Nordin, and Omed Saadallah Al_Amedy. "Higher order thinking skills among secondary school students in science learning." MOJES: Malaysian Online Journal of Educational Sciences 3, no. 3 (2018): 13-20.
Nidaa, Ghalib Ali, and Salih Hammad Dhiyaa. "CLASSIFYING EXAM QUESTIONS BASED ON BLOOM'S TAXONOMY USING MACHINE LEARNING APPROACH." In Технологии разработки информационных систем ТРИС-2019, pp. 260-269. 2019.
Lucas, Kirsten C., Susan M. Dippenaar, and Pieter Hertzog Du Toit. "Analysis of assessment practice and subsequent performance of third year level students in natural sciences." Africa Education Review 11, no. 4 (2014): 563-583.
Husain, Farhat N. "Use of Digital Assessments How to Utilize Digital Bloom to Accommodate Online Learning and Assessments?." Asian Journal of Education and Training 7, no. 1 (2021): 30-35.
Tanalola, H. S., S. Fattahb, S. R. Sulong, and Mazlina Mamat. "Mining Exam Question based on Bloom’s Taxonomy." School of Engineering and Information Technology, University Malaysia (2017): 424-427.
Mohd Effendi Ewan Mohd, Matore. "Rasch model assessment for bloom digital taxonomy applications." Computers, Materials, & Continua (2021): 1235-1253.
Kamlasi, Imanuel. "Descriptive Analyses on English Test Items based on the Application of Revised Bloom’s Taxonomy." Metathesis: Journal of English Language, Literature, and Teaching 2, no. 2 (2018): 203-210
Laddha, Manjushree D., Varsha T. Lokare, Arvind W. Kiwelekar, and Laxman D. Netak. "Classifications of the Summative Assessment for Revised Blooms Taxonomy by using Deep Learning." arXiv preprint arXiv:2104.08819 (2021).
Sami, Joy Christy Antony, and Umamakeswari Arumugam. "A descriptive analysis of students learning skills using bloom’s revised taxonomy." Journal of Computer Science 16, no. 2 (2020): 183-193.
Kumara, B. T. G. S., A. Brahmana, and Incheon Paik. "Bloom's taxonomy and rules based question analysis approach for measuring the quality of examination papers." International Journal of Knowledge Engineering 5, no. 1 (2019): 2-6.
Ullah, Zahid, Adidah Lajis, Mona Jamjoom, Abdulrahman H. Altalhi, Jalal Shah, and Farrukh Saleem. "A rule-based method for cognitive competency assessment in computer programming using Bloom’s taxonomy." IEEE Access 7 (2019): 64663-64675.
Ali, Nidaa Ghalib, and Dhiyaa Salih Hammad. "CLASSIFYING EXAM QUESTIONS BASED ON BLOOM’S TAXONOMY USING MACHINE LEARNING APPROACH."
Ullah, Zahid, Adidah Lajis, Mona Jamjoom, Abdulrahman Altalhi, and Farrukh Saleem. "Bloom's taxonomy: A beneficial tool for learning and assessing students’ competency levels in computer programming using empirical analysis." Computer Applications in Engineering Education 28, no. 6 (2020): 1628-1640.
Prasad, G. N. R. "Identification of Bloom’s Taxonomy level for the given Question paper using NLP Tokenization technique." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 13 (2021): 1872-1875.
Makhlouf, Karima, Lobna Amouri, Nada Chaabane, and EL-Haggar Nahla. "Exam Questions Classification Based on Bloom's Taxonomy: Approaches and Techniques." In 2020 2nd International Conference on Computer and Information Sciences (ICCIS), pp. 1-6. IEEE, 2020.
Atachiants, Roman, Gavin Doherty, and David Gregg. "Parallel performance problems on shared-memory multicore systems: taxonomy and observation." IEEE Transactions on Software Engineering 42, no. 8 (2016): 764-785.
Tanalola, H. S., S. Fattahb, S. R. Sulong, and Mazlina Mamat. "Mining Exam Question based on Bloom’s Taxonomy." School of Engineering and Information Technology, University Malaysia (2017): 424-427.
Uma, D., S. Thenmozhi, and Rabin Hansda. "Analysis on cognitive thinking of an assessment system using revised Bloom's taxonomy." In 2017 5th IEEE International Conference on MOOCs, Innovation and Technology in Education (MITE), pp. 152-159. IEEE, 2017.
Bindra, Simranjeet Kour, Akshay Girdhar, and Inderjeet Singh Bamrah. "Outcome based predictive analysis of automatic question paper using data mining." In 2017 2nd International Conference on Communication and Electronics Systems (ICCES), pp. 629-634. IEEE, 2017.
Jayakodi, Kithsiri, Madhushi Bandara, Indika Perera, and Dulani Meedeniya. "WordNet and Cosine Similarity based Classifier of Exam Questions using Bloom's Taxonomy." International Journal of Emerging Technologies in Learning 11, no. 4 (2016).
Patil, Soumya K., and M. M. Shreyas. "A comparative study of question bank classification based on revised bloom’s taxonomy using svm and k-nn." In 2017 2nd International Conference On Emerging Computation and Information Technologies (ICECIT), pp. 1-7. IEEE, 2017.
Fayyad, Usama M., Gregory Piatetsky-Shapiro, and Padhraic Smyth. "Knowledge Discovery and Data Mining: Towards a Unifying Framework." In KDD, vol. 96, pp. 82-88. 1996.
Rotondo, Anna, and Fergus Quilligan. "Evolution paths for knowledge discovery and data mining process models." SN Computer Science 1, no. 2 (2020): 1-19.
Rogalewicz, Michał, and Robert Sika. "Methodologies of knowledge discovery from data and data mining methods in mechanical engineering." Management and Production Engineering Review (2016).
Yates, Darren, and Md Zahidul Islam. "Data Mining on Smartphones: An Introduction and Survey." ACM Computing Surveys (CSUR) (2022).
Husain, Farhat N. "Use of Digital Assessments How to Utilize Digital Bloom to Accommodate Online Learning and Assessments?." Asian Journal of Education and Training 7, no. 1 (2021): 30-35.
Tanalola, H. S., S. Fattahb, S. R. Sulong, and Mazlina Mamat. "Mining Exam Question based on Bloom’s Taxonomy." School of Engineering and Information Technology, University Malaysia (2017): 424-427.
Mohd Effendi Ewan Mohd, Matore. "Rasch model assessment for bloom digital taxonomy applications." Computers, Materials, & Continua (2021): 1235-1253.
Bhargav, H. S., Gangadhar Akalwadi, and Nitin V. Pujari. "Application of blooms taxonomy in day-to-day examinations." In 2016 IEEE 6th International Conference on Advanced Computing (IACC), pp. 825-829. IEEE, 2016.
Sharfuddin, Abdullah Aziz, Md Nafis Tihami, and Md Saiful Islam. "A deep recurrent neural network with bilstm model for sentiment classification." In 2018 International conference on Bangla speech and language processing (ICBSLP), pp. 1-4. IEEE, 2018.
Ms. Pooja Sahu. (2015). Automatic Speech Recognition in Mobile Customer Care Service. International Journal of New Practices in Management and Engineering, 4(01), 07 - 11. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/34
Mondal , D. . (2021). Remote Sensing Based Classification with Feature Fusion Using Machine Learning Techniques. Research Journal of Computer Systems and Engineering, 2(1), 28:32. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/16
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