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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

Semantic-based enhanced logistic regression model with dual mode classification for accurate MCQs generation

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pp. 281–289Vol. 46Issue 2March 2025DOI: 10.47974/JIOS-1913XML
Received:
13 Nov 2024
Published Online:
17 Mar 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1913
Pages:
281–289

Abstract

The developments in language models and architectures, such as the transformer, allow us to investigate how teachers benefit from these endeavors. In this research, neural language models are employed to generate Multiple Choice Questions (MCQs) automatically from Wikipedia. MCQs are a common form of assessment. Despite the advantages, creating MCQs manually is difficult, time-consuming, and prone to mistakes. This is because the development of multiple-choice questions requires knowledge from the MCQ developers, as each MCQ consists of a question, a correct answer, and other possibilities. This research presents a method that combines semantic and machine learning techniques to create technically accurate MCQ stems of varying sorts. This paper proposes a Semantic-based Enhanced Logistic Regression Model with Dual Mode Classification (SbELR-DMC) for accurate MCQs and Odd one-out model questions for assessment of students. The proposed model when contrasted with the traditional models exhibits better accuracy.

Keywords

Subject Classifications

Primary 93A30Secondary 49K15

References

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