Automatic MCQ Generator Using Natural Language Techniques

IRJET, 2022

Multiple Choice Question (MCQ) is a versatile question type where respondents select one correct answer from the other choices. Generation of such MCQs along with different implausible options is tedious and time-consuming work. Existing automated question generation systems reduce manual efforts to some extent as they were at only sentence level information. By looking widespread usage of MCQs from school exams to competitive exams, automated generation of more reliable questions is required. The proposed system generated questions based on an Extractive summary using the BERT model which holds paragraphlevel context. BLEU score is used as an evaluation matrix to demonstrate the quality of model-generated questions across the human generated questions. The achieved BLEU score is 0.66. Though the proposed system focuses on Fill-inthe-blank questions scope can be extended to various different types of objective questions.

PeerJ Computer Science

Students require continuous feedback for effective learning. Multiple choice questions (MCQs) are extensively used among various assessment methods to provide such feedback. However, manual MCQ generation is a tedious task that requires significant effort, time, and domain knowledge. Therefore, a system must be present that can automatically generate MCQs from the given text. The automatic generation of MCQs can be carried out by following three sequential steps: extracting informative sentences from the textual data, identifying the key, and determining distractors. The dataset comprising of various topics from the 9th and 11th-grade computer science course books are used in this work. Moreover, TF-IDF, Jaccard similarity, quality phrase mining, K-means, and bidirectional encoder representation from transformers techniques are utilized for automatic MCQs generation. Domain experts validated the generated MCQs with 83%, 77%, and 80% accuracy, key generation, and distractor generatio...

International Journal of Electrical and Computer Engineering (IJECE), 2024

The goal of this article is to develop a multiple-choice questions generation system that has a number of advantages, including quick scoring, consistent grading, and a short exam period. To overcome this difficulty, we suggest treating the problem of question creation as a sequence-to-sequence learning problem, where a sentence from a text passage can directly mapped to a question. Our approach is data-driven, which eliminates the need for manual rule implementation. This strategy is more effective and gets rid of potential errors that could result from incorrect human input. Our work on question generation, particularly the usage of the transformer model, has been impacted by recent developments in a number of domains, including neural machine translation, generalization, and picture captioning.

Natural Language Engineering, 2006

This paper describes a novel computer-aided procedure for generating multiple-choice test items from electronic documents. In addition to employing various Natural Language Processing techniques, including shallow parsing, automatic term extraction, sentence transformation and computing of semantic distance, the system makes use of language resources such as corpora and ontologies. It identifies important concepts in the text and generates questions about these concepts as well as multiple-choice distractors, offering the user the option to post-edit the test items by means of a user-friendly interface. In assisting test developers to produce items in a fast and expedient manner without compromising quality, the tool saves both time and production costs.

arXiv (Cornell University), 2023

The use of question-based activities (QBAs) is wide-spread in education, traditionally forming an integral part of the learning and assessment process. In this paper, we design and evaluate an automated question generation tool for formative and summative assessment in schools. We present an expert survey of one hundred and four teachers, demonstrating the need for automated generation of QBAs, as a tool that can significantly reduce the workload of teachers and facilitate personalized learning experiences. Leveraging the recent advancements in generative AI, we then present a modular framework employing transformer based language models for automatic generation of multiple-choice questions (MCQs) from textual content. The presented solution, with distinct modules for question generation, correct answer prediction, and distractor formulation, enables us to evaluate different language models and generation techniques. Finally, we perform an extensive quantitative and qualitative evaluation, demonstrating trade-offs in the use of different techniques and models.

Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing -, 2003

This paper describes a novel computer-aided procedure for generating multiple-choice tests from electronic instructional documents. In addition to employing various NLP techniques including term extraction and shallow parsing, the program makes use of language resources such as a corpus and WordNet. The system generates test questions and distractors, offering the user the option to post-edit the test items.

Computer Applications in Engineering Education, 2019

Knowledge acquisition is the prime objective of a learner from an educational system and evaluating the learner's knowledge is the eventual goal of an examination process. This paper introduces a system which is able to produce fill-in-the-blank questions to test the knowledge of a learner that he or she has accumulated after reading a course material. The question generation task is subdivided into three modules: sentence selection, answer-key identification and question formation along with distractors generation. The sentence is selected using a coarse-grain part-of-speech tagset. The answerkey is extracted by identifying topic-word in the sentence and question is formed by omitting this topic-word from the sentence. This paper also highlights an efficient corpus-based distractors generation technique to produce multiple-choice fill-in-the-blank test items.

Computers & Education: Artificial Intelligence, 2025

The application of learning sciences with technology has been shown to boost learner interactions, yet the potential of advanced tool, particularly those that leverage Natural Language Processing (NLP), still very much untapped in learning contexts. This paper speaks to this age-old problem of generating quality Multiple-Choice questions (MCQs)-a prevalent but time-consuming mode of assessmentvia the suggested comprehensive comparison study of template-based AI solutions. The study contrasts general-purpose Large Language Models (LLMs) with specialized MCQ-focused AI programs. The scientific approach employed was quite stringent, where each of the software applications was benchmarked using a common dataset of text across varying levels of complexity and topic. Results indicate that general-purpose LLMs, especially DeepSeek and ChatGPT, consistently present higher performance and reliability, especially when processing complex textual content. Whereas specialized tools offer distinctive formatting options, they exhibit decreasing performance as texts become more complex and signify strong operation constriction at the free versions. Developing solid and effective distractors turned out to be a complicated task for all the tested tools. We conclude the paper by presenting a standardized assessment model, making evidence-based recommendations for developers and teachers, and suggesting ways to incorporate various AI capabilities into modern educational assessment effectively.

International Journal of Computer Applications, 2014

In this research, an automatic multiple choice question generation system for evaluating semantic role labels and named entities is proposed. The selection of the informative sentence and the keyword to be asked about are based on the semantic labels and named entities that exist in the question sentence. The research introduces a novel method for the distractor selection process. Distractors are chosen based on a string similarity measure between sentences in the data set. Eight algorithms of string similarity measures are used in this research. The system is tested using a set of sentences extracted from the data set for question answering. Experimental results prove that the semantic role labeling and named entity recognition approaches can be used for keyword selection. String similarity measures have been used in generating the distractors in the process of automatic multiple choice questions generation. Combining the similarity measures of some algorithms led to enhancing the results.

Soft Computing, 2013

It is important for students to expertise in their field of study, because there is an agile change in all the domains. Even though resources are available to learn, proper assessment helps them to improve upon their knowledge. In this paper, an automatic generation of multiple choice questions on any user-defined domain is proposed. It first extracts text relevant to the given domain from the web and summarises using fireflies-based preference learning. The sentences in the summary are transformed into stem for the MCQs. The distractors are generated using similarity metrics such as hypernyms and hyponyms. The system also generates analogy questions to test the verbal ability of the students.