Research Article Open Access

A Descriptive Analysis of Students Learning Skills Using Bloom’s Revised Taxonomy

Joy Christy Antony Sami 1 and Umamakeswari Arumugam1
  • 1 SASTRA Deemed to be University, India

Abstract

The academic committees worldwide suggest technical institutions to follow Revised Bloom’s Taxonomy (RBT), a framework that helps to develop learning objectives. The model classifies a hierarchy of educational objectives such as cognitive, sensory and affective domains that are not only helping the students to evolve thinking abilities but also to identify the skills they are lacking with. Analysis of students RBT skills through data mining techniques is more valuable and is yet to be explored. This paper employs predictive and descriptive techniques of data mining to analyze the RBT level of each student. The methodology uses a classifier to classify the RBT level of questions under six levels such as remembering, understanding, applying, analyzing, evaluating, creating and performs clustering of students with respect to overall RBT level and lacking RBT skill of each student. The experimentation is carried out with university students. The results show that the proposed classifier is able to achieve 98% accuracy by correctly classifying RBT levels of input questions. The results also shows that the proposed work creates précised and meaningful clusters of overall RBT level/Lacking RBT skill of each student with precision 0.83 and 0.79 which could help the instructor to design different pedagogical approaches to improve students learning.

Journal of Computer Science
Volume 16 No. 2, 2020, 183-193

DOI: https://doi.org/10.3844/jcssp.2020.183.193

Submitted On: 6 July 2019 Published On: 4 September 2019

How to Cite: Antony Sami , J. C. & Arumugam, U. (2020). A Descriptive Analysis of Students Learning Skills Using Bloom’s Revised Taxonomy. Journal of Computer Science, 16(2), 183-193. https://doi.org/10.3844/jcssp.2020.183.193

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Keywords

  • Bloom’s Taxonomy
  • Cognitive
  • Psychomotor
  • Affective
  • Clustering Techniques
  • K-Means Algorithm