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Question Classification Based on Bloom’s Taxonomy Using Enhanced TF-IDF

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@article{IJASEIT6835,
   author = {Manal Mohammed and Nazlia Omar},
   title = {Question Classification Based on Bloom’s Taxonomy Using  Enhanced TF-IDF},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {8},
   number = {4-2},
   year = {2018},
   pages = {1679--1685},
   keywords = {question classification; bloom’s taxonomy; TF-IDF; support vector machine; naïve bayes; K-Nearest Neighbour.},
   abstract = {Bloom’s Taxonomy has been used widely in the educational environment to measure, evaluate and write high-quality exams. Therefore, many researchers have worked on the automation for classification of exam questions based on Bloom’s Taxonomy. The aim of this study is to make an enhancement for one of the most popular statistical feature, which is TF-IDF, to improve the performance of exam question classification in accordance to Bloom’s Taxonomy cognitive domain. Verbs play an important role in determining the level of a question in Bloom’s Taxonomy. Thus, the improved method assigns the impact factor for the words by taking the advantage of the part-of-speech tagger. The higher impact factor assigns to the verbs, then to the noun and adjective, after that, the lower impact factor assigns to the other part-of-speech. The dataset that has been used in this study is consist of 600 questions, divided evenly into each Bloom level. The questions first pass into the preprocessing phase in which they are prepared to be suitable for applying the proposed enhanced feature. For classification purpose, three machine learning classifiers are used Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour. The enhanced feature shows satisfactory result by outperforming the classical feature TF-IDF via all classifiers in terms of weighted recall, precision, and F1-measure. On the other hand, Support Vector Machine has superior performance over other classifiers Naïve Bayes, and K-Nearest Neighbour by achieving an average of 86%, 85%, and 81.6% weighted F1-measure respectively. However, these results are promising and encouraging for further investigations.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6835},
   doi = {10.18517/ijaseit.8.4-2.6835}
}

EndNote

%A Mohammed, Manal
%A Omar, Nazlia
%D 2018
%T Question Classification Based on Bloom’s Taxonomy Using  Enhanced TF-IDF
%B 2018
%9 question classification; bloom’s taxonomy; TF-IDF; support vector machine; naïve bayes; K-Nearest Neighbour.
%! Question Classification Based on Bloom’s Taxonomy Using  Enhanced TF-IDF
%K question classification; bloom’s taxonomy; TF-IDF; support vector machine; naïve bayes; K-Nearest Neighbour.
%X Bloom’s Taxonomy has been used widely in the educational environment to measure, evaluate and write high-quality exams. Therefore, many researchers have worked on the automation for classification of exam questions based on Bloom’s Taxonomy. The aim of this study is to make an enhancement for one of the most popular statistical feature, which is TF-IDF, to improve the performance of exam question classification in accordance to Bloom’s Taxonomy cognitive domain. Verbs play an important role in determining the level of a question in Bloom’s Taxonomy. Thus, the improved method assigns the impact factor for the words by taking the advantage of the part-of-speech tagger. The higher impact factor assigns to the verbs, then to the noun and adjective, after that, the lower impact factor assigns to the other part-of-speech. The dataset that has been used in this study is consist of 600 questions, divided evenly into each Bloom level. The questions first pass into the preprocessing phase in which they are prepared to be suitable for applying the proposed enhanced feature. For classification purpose, three machine learning classifiers are used Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour. The enhanced feature shows satisfactory result by outperforming the classical feature TF-IDF via all classifiers in terms of weighted recall, precision, and F1-measure. On the other hand, Support Vector Machine has superior performance over other classifiers Naïve Bayes, and K-Nearest Neighbour by achieving an average of 86%, 85%, and 81.6% weighted F1-measure respectively. However, these results are promising and encouraging for further investigations.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6835
%R doi:10.18517/ijaseit.8.4-2.6835
%J International Journal on Advanced Science, Engineering and Information Technology
%V 8
%N 4-2
%@ 2088-5334

IEEE

Manal Mohammed and Nazlia 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, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.4-2.6835.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Mohammed, Manal
AU  - Omar, Nazlia
PY  - 2018
TI  - Question Classification Based on Bloom’s Taxonomy Using  Enhanced TF-IDF
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 4-2
Y2  - 2018
SP  - 1679
EP  - 1685
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - question classification; bloom’s taxonomy; TF-IDF; support vector machine; naïve bayes; K-Nearest Neighbour.
N2  - Bloom’s Taxonomy has been used widely in the educational environment to measure, evaluate and write high-quality exams. Therefore, many researchers have worked on the automation for classification of exam questions based on Bloom’s Taxonomy. The aim of this study is to make an enhancement for one of the most popular statistical feature, which is TF-IDF, to improve the performance of exam question classification in accordance to Bloom’s Taxonomy cognitive domain. Verbs play an important role in determining the level of a question in Bloom’s Taxonomy. Thus, the improved method assigns the impact factor for the words by taking the advantage of the part-of-speech tagger. The higher impact factor assigns to the verbs, then to the noun and adjective, after that, the lower impact factor assigns to the other part-of-speech. The dataset that has been used in this study is consist of 600 questions, divided evenly into each Bloom level. The questions first pass into the preprocessing phase in which they are prepared to be suitable for applying the proposed enhanced feature. For classification purpose, three machine learning classifiers are used Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour. The enhanced feature shows satisfactory result by outperforming the classical feature TF-IDF via all classifiers in terms of weighted recall, precision, and F1-measure. On the other hand, Support Vector Machine has superior performance over other classifiers Naïve Bayes, and K-Nearest Neighbour by achieving an average of 86%, 85%, and 81.6% weighted F1-measure respectively. However, these results are promising and encouraging for further investigations.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6835
DO  - 10.18517/ijaseit.8.4-2.6835

RefWorks

RT Journal Article
ID 6835
A1 Mohammed, Manal
A1 Omar, Nazlia
T1 Question Classification Based on Bloom’s Taxonomy Using  Enhanced TF-IDF
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 4-2
YR 2018
SP 1679
OP 1685
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 question classification; bloom’s taxonomy; TF-IDF; support vector machine; naïve bayes; K-Nearest Neighbour.
AB Bloom’s Taxonomy has been used widely in the educational environment to measure, evaluate and write high-quality exams. Therefore, many researchers have worked on the automation for classification of exam questions based on Bloom’s Taxonomy. The aim of this study is to make an enhancement for one of the most popular statistical feature, which is TF-IDF, to improve the performance of exam question classification in accordance to Bloom’s Taxonomy cognitive domain. Verbs play an important role in determining the level of a question in Bloom’s Taxonomy. Thus, the improved method assigns the impact factor for the words by taking the advantage of the part-of-speech tagger. The higher impact factor assigns to the verbs, then to the noun and adjective, after that, the lower impact factor assigns to the other part-of-speech. The dataset that has been used in this study is consist of 600 questions, divided evenly into each Bloom level. The questions first pass into the preprocessing phase in which they are prepared to be suitable for applying the proposed enhanced feature. For classification purpose, three machine learning classifiers are used Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour. The enhanced feature shows satisfactory result by outperforming the classical feature TF-IDF via all classifiers in terms of weighted recall, precision, and F1-measure. On the other hand, Support Vector Machine has superior performance over other classifiers Naïve Bayes, and K-Nearest Neighbour by achieving an average of 86%, 85%, and 81.6% weighted F1-measure respectively. However, these results are promising and encouraging for further investigations.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6835
DO  - 10.18517/ijaseit.8.4-2.6835