Natural Language Processing Techniques for Textbook Analysis

EasyChair Preprint 14263

Abstract

Natural Language Processing (NLP) techniques offer powerful tools for analyzing and understanding textbooks, significantly enhancing educational research and pedagogical strategies. This paper explores various NLP methodologies applied to textbook analysis, including text classification, sentiment analysis, and topic modeling. By leveraging algorithms such as Latent Dirichlet Allocation (LDA) for topic discovery and Named Entity Recognition (NER) for extracting relevant information, educators and researchers can gain deeper insights into the content, structure, and thematic evolution of textbooks. We also examine the use of word embeddings and language models to assess readability and identify key concepts, ultimately aiming to improve curriculum design and instructional materials. The paper concludes with a discussion on the potential of NLP to transform textbook analysis and the implications for future educational technology developments.

Keyphrases: Named Entity Recognition, Natural Language Processing, Text Preprocessing, Text Summarization, semantic analysis, text classification, textbook analysis

@booklet{EasyChair:14263,
  author    = {John Owen},
  title     = {Natural Language Processing Techniques for Textbook Analysis},
  howpublished = {EasyChair Preprint 14263},
  year      = {EasyChair, 2024}}