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A question-answer generation system for an asynchronous distance learning platform.

Hei-Chia Wang1,2, Martinus Maslim1,3, Chia-Hao Kan1

  • 1Institute of Information Management, College of Management, National Cheng Kung University, Tainan City, Taiwan.

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Summary
This summary is machine-generated.

This study introduces an automated question generation model for asynchronous distance learning. The asynchronous distance teaching-question generation (ADT-QG) model enhances student engagement and aids teacher assessment in online education.

Keywords:
Distance learningQuestion generationSentences-BERT (SBERT)T5

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Area of Science:

  • Educational Technology
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Distance learning offers flexibility but faces engagement challenges, particularly in asynchronous formats.
  • Asynchronous learning limits student-teacher interaction, hindering comprehension assessment.
  • Automated question generation can bridge this gap, improving engagement and evaluation.

Purpose of the Study:

  • To develop an automated system for generating questions from asynchronous learning materials.
  • To create multiple-choice questions for easier student answering and teacher grading.
  • To enhance the effectiveness of asynchronous distance education.

Main Methods:

  • Proposed the asynchronous distance teaching-question generation (ADT-QG) model.
  • Integrated Sentences-BERT (SBERT) for high-similarity sentence-to-question generation.
  • Utilized the Transfer Text-to-Text Transformer (T5) model with a Wiki corpus for fluent and relevant question generation.

Main Results:

  • The ADT-QG model demonstrates good fluency and clarity in generated questions.
  • Generated questions show relevance to the instructional content.
  • The model effectively addresses the need for engagement and assessment in asynchronous learning.

Conclusions:

  • The ADT-QG model is a promising tool for improving asynchronous distance learning.
  • Automated question generation can significantly enhance student comprehension and teacher feedback.
  • This approach supports more effective online educational experiences.