Automatic Question Paper Generation, according to Bloom’s Taxonomy, by generating questions from text using Natural Language Processing


Authors : Shivali Joshi; Parin Shah; Sahil Shah

Volume/Issue : Volume 6 - 2021, Issue 4 - April

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/3gT3dAI

- The ongoing research on "Natural Language Processing and its applications in the educational domain”, has witnessed various approaches for question generation from paragraphs. Despite the existence of numerous techniques for the automatic generation of questions, only a few have been implemented in real classroom settings. This research paper reviews existing methods and presents an AQGS (Automatic Question Generation System) that uses Natural Language Processing Libraries like NLTK and Spacy to suggest questions from a passage provided as an input. The Question Paper is generated by randomly selecting questions for a specific level of Bloom’s Taxonomy. We conclude by determining the efficacy of the AQGS using performance measures like accuracy, precision, and recall.

Keywords : Question Generation, Bloom’s Taxonomy, Natural Language Processing (NLP), Natural Language Toolkit (NLTK), Spacy, POS Tagging, Named Entity Recognizer (NER).

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Paper Submission Last Date
28 - February - 2023

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