The Effect of Bloom’s Taxonomy on Random Forest Classifier for cognitive level identification of eLearning content

EasyChair Preprint 2188

Abstract

With the advancement in internet, the efficiency of e-learning increased and currently e-learning is one of the primary method of learning for most learners after the regular academics studies.  The knowledge delivery through e-learning web sites increased exponentially over the years because of the advancement in internet and e-learning technologies. The learner can find many website with lots of information on the relevant domain. However learners often found it difficult to figure out the right leaning content from the humongous availability of e-content.  In the proposed work an intelligent framework is developed to address this issue. The framework recommend the right learning content to a user from the e-learning web sites with the knowledge level of the user. The e-contents available in web sites were divided in to three cognitive levels   such as beginner, intermediate and advanced level. The current work uses Blooms Taxonomy verbs and its synonyms to improve the accuracy of the classifier used in the framework.

Keyphrases: Blooms Taxonomy, Cognitive Level, POS tagging, Random Forest Classifier, decision trees, learning, machine learning

@booklet{EasyChair:2188,
  author    = {Benny Thomas and J Chandra},
  title     = {The Effect of Bloom’s Taxonomy on Random Forest Classifier for cognitive level identification of  eLearning content},
  howpublished = {EasyChair Preprint 2188},
  year      = {EasyChair, 2019}}