CFP last date
21 October 2024
Reseach Article

Ensemble Classifier based Approach for Classification of Examination Questions into Bloom’s Taxonomy Cognitive Levels

by K. A. Osadi, M. G. N. A. S. Fernando, W. V. Welgama
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 4
Year of Publication: 2017
Authors: K. A. Osadi, M. G. N. A. S. Fernando, W. V. Welgama
10.5120/ijca2017913328

K. A. Osadi, M. G. N. A. S. Fernando, W. V. Welgama . Ensemble Classifier based Approach for Classification of Examination Questions into Bloom’s Taxonomy Cognitive Levels. International Journal of Computer Applications. 162, 4 ( Mar 2017), 1-6. DOI=10.5120/ijca2017913328

@article{ 10.5120/ijca2017913328,
author = { K. A. Osadi, M. G. N. A. S. Fernando, W. V. Welgama },
title = { Ensemble Classifier based Approach for Classification of Examination Questions into Bloom’s Taxonomy Cognitive Levels },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 4 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number4/27228-2017913328/ },
doi = { 10.5120/ijca2017913328 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:08:02.498432+05:30
%A K. A. Osadi
%A M. G. N. A. S. Fernando
%A W. V. Welgama
%T Ensemble Classifier based Approach for Classification of Examination Questions into Bloom’s Taxonomy Cognitive Levels
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 4
%P 1-6
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The concept of Bloom’s taxonomy cognitive domain has been broadly used as a guideline in preparing a reasonable examination paper that consists of questions belonging to various cognitive levels which are helpful in evaluating different capabilities of students. Currently, academicians identify Bloom’s taxonomy cognitive level manually, but that is a tedious and a time-consuming task. Therefore, the use of automatic classification technique based on Bloom’s taxonomy cognitive levels is highly needed. Several studies have been carried out to fulfill this task, but most of these studies have failed to address the overlapping keyword problem among Bloom’s taxonomy cognitive levels, and most of these studies have not considered the semantic structure of the examination questions. To overcome these problems, this study proposes a question classification model using an ensemble classifier approach by combining four different classifiers; namely rule based, support vector machine, k-nearest neighbor and Naive Bayes.The results of four different classifiers are integrated to derive the final corresponding Bloom’s taxonomy cognitive level, using majority voting and WordNet similarity values. WordNet similarity is used to explore the semantic structure of the examination questions. A sample of first year programming examination questions of University of Colombo School of Computing was used for the evaluation. Four domain experts confirmed identified Bloom’s taxonomy cognitive levels of the questions in the dataset. The experimental results indicate that the proposed ensemble classifier approach yields much better accuracy than the accuracy of the individual classifiers.

References
  1. Syahidah Sufi Haris and Nazlia Omar . Determining Cognitive Category of Programming Question with Rule-based Approach. International Journal of Information Processing and Management, 4(3):86–95, 2013.
  2. Dhuha Abdulhadi Abduljabbar and Nazlia Omar. Exam questions classification based on Bloom’s taxonomy cognitive level using classifiers combination. Journal of Theoretical and Applied Information Technology, 78(3):447–455, 2015.
  3. Anwar Ali Yahaya Addin Osman. CLASSIFICATIONS of EXAM QUESTIONS USING LINGUISTICALLY- MOTIVATED FEATURES : A CASE STUDY BASED on BLOOM ’ S TAXONOMY Research Questions Research Aim. In The Sixth International Arab Conference on Quality Assurance in Higher Education, volume 2016, Saudi Arabia, 2016.
  4. Ibtihal R. Assaly and Oqlah M. Smadi. Using bloom’s taxonomy to evaluate the cognitive levels of master class textbook’s questions. English Language Teaching, 8(5):100–110, 2015.
  5. Wen Chih Chang and Ming Shun Chung. Automatic applying Bloom’s taxonomy to classify and analysis the cognition level of english question items. 2009 Joint Conferences on Pervasive Computing, JCPC 2009, pages 727–733, 2009.
  6. Ali Danesh, Behzad Moshiri, and Omid Fatemi. Improve text classification accuracy based on classifier fusion methods. 2007 10th International Conference on Information Fusion, pages 1–6, 2007.
  7. Eman Ghanem Nayef, Nik Rosila, Nik Yaacob, and Hairul Nizam Ismail. Taxonomies of Educational Objective Domain. International Journal of Academic Research in Business and Social Sciences, 3(9):2222–6990, 2013.
  8. Syahidah Sufi Haris and Nazlia Omar. Bloom’s taxonomy question categorization using rules and N-gram approach. Journal of Theoretical and Applied Information Technology, 76(3):401–407, 2015.
  9. K. Jayakodi, M. Bandara, I. Perera, and D. Meedeniya.Word- Net and cosine similarity based classifier of exam questions using bloom’s taxonomy. International Journal of Emerging Technologies in Learning, 11(4):142–149, 2016.
  10. L Moreira-Matias and J Mendes-Moreira. Text categorization using an ensemble classifier based on a mean co-association matrix. Technical report, 2012.
  11. Lior Rokach. Ensemble-based classifiers. Technical Report November 2009, 2010.
  12. Anbuselvan Sangodiah, Rohiza Ahmad, Wan Fatimah, and Wan Ahmad. A Review in Feature Extraction Approach in Question Classification Using Support Vector Machine. 2014 IEEE International Conference on Control System, Computing and Engineering, (November):536–541, 2014.
  13. Julio Villena Rom´an, Sonia Collada P´erez, Sara Lana Serrano, and Jos´e Carlos Gonz´alez Crist´obal. Hybrid Approach Combining Machine Learning and a Rule-Based Expert System for Text Categorization. In Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference—Twenty-Fourth International Florida Artificial Intelligence Research Society Conference — 18/05/2011 - 20/05/2011—Palm Beach, Florida, EEUU, pages 323–328, 2011.
  14. AA Yahya and A Osman. Automatic classification of questions into Bloom’s cognitive levels using support vector machines. In The International Arab Conference on . . . , number December 2011, 2011.
  15. Norazah Yusof and Chai Jing Hui. Determination of Bloom ’ s Cognitive Level of Question Items using Artificial Neural Network. In 10th International Conference on Intelligent Systems Design and Applications (ISDA), pages 866–870, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Bloom’s taxonomy Ensemble Classifier k-nearest neighbor Naive Bayes Natural Language Processing Rule based Support Vector Machine