Remediating textbook deficiencies by leveraging community question answers

Area of Science:

• Educational Technology
• Computer Science
• Information Science

Background:

• Textbooks often contain conceptual gaps, hindering student comprehension.
• Community question-answering (cQA) forums provide valuable, illustrative content for complex concepts.

Purpose of the Study:

• To develop and evaluate a system for recommending augmentations to address conceptual deficiencies in textbooks.
• To leverage cQA data for creating effective supplementary learning materials.

Main Methods:

• A supervised machine learning approach was used to identify conceptual gaps based on 12 discourse features.
• The system retrieves and filters community question-answering pairs for concept augmentation (basic and supplementary).
• A web-based interface was developed to present these augmentations, evaluated using expert opinions and student pretest-posttest assessments.

Main Results:

• The proposed system successfully identified conceptual deficiencies in 28 Indian textbooks.
• Both system and human evaluations confirmed that augmentations addressed concept-specific gaps and stimulated learning interest.
• The learning interface was found to be user-friendly and effective in presenting the augmentations.

Conclusions:

• The developed system offers a viable method for augmenting textbook content using cQA data.
• The augmentations effectively bridge conceptual gaps and enhance student engagement.
• The user-friendly interface facilitates the delivery of these educational enhancements.