Analyzing Textbook Content Using Natural Language Processing Techniques

EasyChair Preprint 14641

Abstract

The advent of Natural Language Processing (NLP) techniques has revolutionized the analysis of textual data across various domains. This study focuses on analyzing textbook content using advanced NLP methodologies. The primary objective is to extract meaningful patterns and insights from educational materials, which can enhance learning outcomes and curriculum development. We employ a combination of text preprocessing, tokenization, and feature extraction methods to prepare the data for analysis. Subsequently, various NLP models, including topic modeling, sentiment analysis, and named entity recognition, are utilized to explore the structure and semantic relationships within the textbooks. Our findings indicate that NLP can effectively identify key concepts, prevalent themes, and the sentiment of educational content. Additionally, the study highlights the potential of NLP in personalizing education by tailoring content to meet individual learning needs. The implications of this research are significant for educators, curriculum designers, and educational technologists, offering a data-driven approach to improving educational content and strategies.

Keyphrases: Classification, Clustering, Gunning Fog Index, Text Summarization, curriculum development, educational technology, personalized learning

@booklet{EasyChair:14641,
  author    = {Kayode Sheriffdeen},
  title     = {Analyzing Textbook Content Using Natural Language Processing Techniques},
  howpublished = {EasyChair Preprint 14641},
  year      = {EasyChair, 2024}}