An Automated Multiple-Choice Question Generation Using Natural Language Processing Techniques

TL;DR

This paper presents an NLP-based system for automatic multiple-choice question generation from lesson materials, aiming to assist teachers by reducing manual effort and improving question relevance.

Contribution

It introduces an automated keyword extraction method for MCQ generation and validates its effectiveness using real lesson materials.

Findings

System successfully extracts relevant keywords for question generation.

Automated keywords closely match teacher-extracted keywords.

User interface facilitates easy access to generated questions.

Abstract

Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.