International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 06 | Jun 2024
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p-ISSN: 2395-0072
Automatic MCQ Generation Using Machine Learning Algorithm Ganesh Wani1, Kulashri Kale2, Deep Kale3 , Shivam Shinde4 1Professor, Department of Artificial Intelligence and Data Science, AISSMS Institute of Information Technology,
Pune, Maharashtra, India
2Student, Department of Artificial Intelligence and Data Science, AISSMS Institute of Information Technology,
Pune, Maharashtra, India Student, Department of Artificial Intelligence and Data Science, AISSMS Institute of Information Technology, Pune, Maharashtra, India 4Student, Department of Artificial Intelligence and Data Science, AISSMS Institute of Information Technology, Pune, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------3
Abstract - Automatic multiple-choice question (MCQ)
and content creators struggling to meet the demand for engaging and effective assessments while facing mounting workloads and resource constraints.
generation is a challenging task in natural language processing (NLP). It involves generating correct and relevant questions from textual data, such as textbooks, articles, or lecture notes. Manual creation of MCQs is a time-consuming and challenging task for teachers, so automatic MCQ generation can be a valuable tool for education. There are a number of different machine learning algorithms that can be used for automatic MCQ generation. One common approach is to use a rule-based system. This involves creating a set of rules that define the different types of MCQs that can be generated, and then applying these rules to the input text.
This approach has the potential to benefit a wide range of domains, including 1.Reduced workload for educators and content creators: Our system saves educators time and resources by automating the tedious task of manually creating MCQs, allowing them to focus on higher-order instructional activities. 2.Increased assessment quality and consistency: Our system uses NLP algorithms and pre-defined learning objectives to ensure that generated MCQs are well-formed, relevant, and consistently aligned with the desired learning outcomes.
In this project, we investigate how to automatically generate multiple-choice questions (MCQs) from textual content using natural language processing (NLP) approaches. Our system preprocesses input text, identifies key information, and formulates relevant MCQs with distractors. By leveraging NLP models and algorithms, we aim to facilitate the creation of engaging and informative assessments, enhancing educational and evaluative processes across various domains.
3.Improved accessibility and personalization: Our system's data-driven nature enables the creation of personalized assessments that are tailored to the needs and strengths of individual learners, promoting inclusivity and deeper learning. 4.Rich data insights for formative assessments: Our system analyses question performance and learner responses, providing valuable data to inform instructional decisions.
Key Words: automatic MCQ generation, natural language processing (NLP), machine learning, education, assessment, evaluation, textual content, key information, relevant MCQs, distractors, engaging, informative.
The development of an effective NLP-powered MCQ generation system is not without its challenges. Ensuring the generation of high-quality questions, mitigating potential bias, and adapting to diverse domains and content types are some critical areas that require careful consideration and ongoing research. Nevertheless, the potential benefits of this technology are undeniable, paving the way for a future where assessment becomes a seamless and personalized extension of the learning experience itself.
1.INTRODUCTION The landscape of education and assessment is rapidly evolving, driven by technological advancements and the ever-growing demand for efficient, scalable, and personalized learning experiences. In this evolving environment, the humble multiple-choice question (MCQ) remains a cornerstone of assessment, offering advantages of standardization, ease of scoring, and the ability to cover a wide range of knowledge domains. However, the traditional process of manually crafting high-quality MCQs is often timeconsuming, laborious, and prone to inconsistencies. This presents a significant challenge, particularly for educators
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Impact Factor value: 8.226
This research paper presents a novel NLP-powered system for automatic MCQ generation. Our aim is to simplify the process of creating assessments, whether for educational purposes or content evaluation, by harnessing the power of NLP to extract pertinent information, formulate questions, and provide plausible answer choices. Through this
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