Analyzing Textbook Content with Natural Language Processing

EasyChair Preprint 14262

Abstract

This study explores the application of Natural Language Processing (NLP) techniques to analyze textbook content, aiming to enhance educational resources and provide deeper insights into pedagogical methods. By leveraging NLP tools, we examine the linguistic structure, thematic coherence, and pedagogical strategies embedded in various textbooks. The analysis focuses on several key areas: the readability and complexity of the text, the distribution and coverage of core concepts, and the identification of latent topics and their interrelations. We employ methods such as text mining, topic modeling, sentiment analysis, and semantic similarity to extract meaningful patterns and trends from the textbook data. Our findings reveal significant variations in content presentation across different subjects and educational levels, highlighting potential areas for content optimization and curriculum development. Furthermore, the study demonstrates the potential of NLP in creating adaptive learning systems that can tailor educational content to individual student needs, thereby fostering more effective learning experiences. This research contributes to the growing body of literature on educational data mining and underscores the transformative potential of NLP in the field of education.

Keyphrases: Named Entity Recognition, Natural Language Processing, Sentiment Analysis, Text Preprocessing, Text Summarization, text analysis, topic modeling

@booklet{EasyChair:14262,
  author    = {John Owen},
  title     = {Analyzing Textbook Content with Natural Language Processing},
  howpublished = {EasyChair Preprint 14262},
  year      = {EasyChair, 2024}}