Classifications of the Summative Assessment for Revised Blooms Taxonomy by using Deep Learning

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.

[1]  Hong Liang,et al.  Text feature extraction based on deep learning: a review , 2017, EURASIP Journal on Wireless Communications and Networking.

[2]  G. C. Hazarika,et al.  Prediction Model on Student Performance based on Internal Assessment using Deep Learning , 2019, Int. J. Emerg. Technol. Learn..

[3]  Cihan Kaleli,et al.  A review on deep learning for recommender systems: challenges and remedies , 2018, Artificial Intelligence Review.

[4]  Dominik Bork,et al.  A Framework for Teaching Conceptual Modeling and Metamodeling Based on Bloom's Revised Taxonomy of Educational Objectives , 2019, HICSS.

[5]  Jianfeng Gao,et al.  Deep Learning Based Text Classification: A Comprehensive Review , 2020, ArXiv.

[6]  Borja Navarro-Colorado,et al.  A Systematic Review of Deep Learning Approaches to Educational Data Mining , 2019, Complex..

[7]  Arvind W. Kiwelekar,et al.  On Which Skills do Indian Universities Evaluate Software Engineering Students? , 2016, ArXiv.

[8]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[9]  Soumya K. Patil,et al.  A Comparative Study of Question Bank Classification based on Revised Bloom’s Taxonomy using SVM and K-NN , 2017, 2017 2nd International Conference On Emerging Computation and Information Technologies (ICECIT).

[10]  Erik Cambria,et al.  A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks , 2016, COLING.

[11]  Salem Alelyani,et al.  Predicting academic performance of students from VLE big data using deep learning models , 2020, Comput. Hum. Behav..

[12]  Chris Piech,et al.  Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning , 2017, EDM.

[13]  D. Krathwohl A Revision of Bloom's Taxonomy: An Overview , 2002 .

[14]  Henryk Maciejewski,et al.  Deep learning methods for subject text classification of articles , 2017, 2017 Federated Conference on Computer Science and Information Systems (FedCSIS).

[15]  Samit Bhattacharya,et al.  Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset , 2017, AAAI.

[16]  Nazlia Omar,et al.  Question classification based on Bloom’s taxonomy cognitive domain using modified TF-IDF and word2vec , 2020, PloS one.