Classifications of the Summative Assessment for Revised Bloom’s Taxonomy by using Deep Learning

Classifications of the Summative Assessment for Revised Bloom’s Taxonomy by using Deep Learning

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2021 by IJETT Journal
Volume-69 Issue-3
Year of Publication : 2021
Authors : Manjushree D. Laddha, Varsha T. Lokare, Arvind W. Kiwelekar, Laxman D. Netak
DOI :  10.14445/22315381/IJETT-V69I3P232

Citation 

MLA Style: Manjushree D. Laddha, Varsha T. Lokare, Arvind W. Kiwelekar, Laxman D. Netak"Classifications of the Summative Assessment for Revised Bloom’s Taxonomy by using Deep Learning" International Journal of Engineering Trends and Technology 69.3(2021):211-218. 

APA Style:Manjushree D. Laddha, Varsha T. Lokare, Arvind W. Kiwelekar, Laxman D. Netak. Classifications of the Summative Assessment for Revised Bloom’s Taxonomy by using Deep Learning  International Journal of Engineering Trends and Technology, 69(3),211-218.

Abstract
Education is the basic step of understanding the truth and the preparation of the intelligence to reflect. Focused on the rational capacity of the human being, the Cognitive process and knowledge dimensions of Revised Bloom’s Taxonomy helps to differentiate the procedure of studying into six types of various cognitive processes and four types of knowledge dimensions. These types are synchronized in the increasing level of difficulty. In this paper, Software Engineering courses of B.Tech Computer Engineering and Information Technology offered by various Universities and Educational Institutes have been investigated for Revised Bloom’s Taxonomy (RBT). Questions are a very useful constituent. Knowledge, intelligence, and strength of the learners can be tested by applying questions. The fundamental goal of this paper is to create a relative study of the classification of the summative assessment based on Revised Bloom’s Taxonomy using the Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) of Deep Learning techniques, in an endeavor to attain significant accomplishment and elevated precision levels.

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Keywords
Revised Bloom’s Taxonomy, Deep Learning, Software Engineering, Convolutional Neural Networks, Long Short-Term Memory.