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

TL;DR

This paper explores classifying summative assessment questions in education using deep learning models, specifically CNN and LSTM, to improve accuracy in evaluating knowledge levels based on Bloom's Revised Taxonomy.

Contribution

It introduces a novel approach applying CNN and LSTM models to classify assessment questions according to Bloom's Revised Taxonomy, enhancing assessment analysis accuracy.

Findings

Achieved high classification accuracy with CNN and LSTM models.

Demonstrated effectiveness of deep learning in educational assessment classification.

Provided a framework for automated question classification based on cognitive levels.

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 Blooms 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 Blooms 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 Blooms 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.