Applying NLP to Examine Textbook Material

EasyChair Preprint 14280

Abstract

The integration of Natural Language Processing (NLP) into educational technology has opened new avenues for examining and enhancing textbook material. This study explores the application of NLP techniques to analyze and evaluate the content of textbooks. By leveraging tools such as text classification, sentiment analysis, and topic modeling, we aim to gain insights into the readability, coherence, and thematic structure of educational texts. Our research focuses on identifying key themes, detecting biases, and assessing the alignment of textbook material with curriculum standards. We employ a combination of supervised and unsupervised learning algorithms to process and interpret large volumes of text data. Preliminary results indicate that NLP can effectively highlight discrepancies and areas for improvement within textbooks, providing educators with actionable feedback to optimize learning materials. This paper discusses the methodologies employed, presents findings from our analysis, and outlines potential implications for the future of educational content development. The study underscores the potential of NLP as a transformative tool in the educational sector, fostering a more tailored and effective learning experience for students.

Keyphrases: Curriculum alignment, Named Entity Recognition, Natural Language Processing, Sentiment Analysis, Text Summarization, readability assessment, textbook analysis, topic modeling

@booklet{EasyChair:14280,
  author    = {Oluwaseun Abiade},
  title     = {Applying NLP to Examine Textbook Material},
  howpublished = {EasyChair Preprint 14280},
  year      = {EasyChair, 2024}}