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Automatic computer science domain multiple-choice questions generation based on informative sentences.

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  • 1Department of Computer Science, National Textile University, Faisalabad, Pakistan.

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Summary
This summary is machine-generated.

This study introduces an automated system for generating multiple-choice questions (MCQs) from text, simplifying assessment for educators. The developed tool achieved 80% accuracy, aiding students and teachers in creating effective learning materials.

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BERTMultiple choice questionsNatural language processingTF-IDFText analysis

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Area of Science:

  • Educational Technology
  • Natural Language Processing
  • Computer Science Education

Background:

  • Effective learning necessitates continuous student feedback.
  • Manual generation of multiple-choice questions (MCQs) is time-consuming and requires expertise.
  • Automated MCQ generation systems can alleviate these challenges.

Purpose of the Study:

  • To develop and evaluate a system for automatic MCQ generation from textual content.
  • To streamline the creation of educational assessments for computer science courses.

Main Methods:

  • Utilized TF-IDF, Jaccard similarity, quality phrase mining, K-means, and bidirectional encoder representation from transformers (BERT).
  • Dataset comprised computer science course materials for 9th and 11th grades.
  • Employed a three-step process: informative sentence extraction, key identification, and distractor generation.

Main Results:

  • Domain experts validated key generation with 83% accuracy and distractor generation with 80% accuracy.
  • The overall automated MCQ generation system achieved 80% accuracy.
  • A desktop application was developed for user-friendly MCQ generation and visualization.

Conclusions:

  • The automated MCQ generation system effectively produces accurate assessment questions.
  • The developed tool offers a valuable resource for educators, students, and educational institutions.
  • This technology can significantly reduce the effort required for creating formative assessments.