World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , Web of Science Group (Under Process) , Directory of Research Journals Indexing , Scholar Article Journal Index (SAJI) , International Scientific Indexing ( ISI ) , 

World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

News & Updation

  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

  • ICV

    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level

  • WJERT New Impact Factor

    Its our Pleasure to Inform you that WJERT Impact Factor has been increased from  5.549 to 5.924 due to high quality Publication at International Level


    JANUARY 2022 Issue has been successfully launched on 1 January 2022.

  • New Issue Published

    Its Our pleasure to inform you that, WJERT 1 January 2022 Issue has been Published, Kindly check it on




Asha B. Sattane*


An important component of question answering systems is question categorization. Questions are provided to fulfill learning objectives in the subject content learned by students. Challenging thing in question answering system is to prepare good quality questions. Quality questions are prepared by assigning cognitive level. Learning and assessment are the two sides of education system. Thus, Bloom's taxonomy is common reference point for it. Exam questions categorization presents a main challenge in categorizing short questions which will have small text. Short questions text are very sparse and far in terms of features also. In order to solve this issue, methodology is proposed to categorize exam questions automatically to the cognitive levels of Bloom's taxonomy. This provides a strategy based methodology using three machine learning classifiers. The classifiers adopted in this work are, Support Vector Machine (SVM),Naive Byes (NB), and k-Nearest Neighbour (k-NN) .The study found that applying feature selection methods, namely Chi-square, Mutual Information and Odd Ratio on question categorization not only make categorization more time efficient, but it also improves the categorization accuracy . Furthermore, it is discovered that combination of classifiers can be applied to categorize the question with feature selection methods.

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