An Automatic Question Generation System Using Rule-Based Approach in Bloom’s Taxonomy

Background: In this competing era, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching- Learning Process (TLP). An assessment is one way to identify the learner’s weak spot in the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. To generate questions is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Objective: The proposed system is to generate the test questions that are mapped with bloom’s taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest bloom’s cognitive levels. Methods: Proposed an Automatic Question Generation (AQG) system which, automatically generates the assessment questions from the input file Dynamically. Results & Conclusion: The outputs are dynamic to create a different set of questions at each execution. Here, the input paragraph is selected from the computer science domain and their output efficiency is measured using precision and recall.