This study addresses a significant gap in educational research by employing statistical methods to measure the sequencing of different components and content topics in textbooks. It is well-documented that the structure and sequencing features of textbooks play a crucial role in enhancing students’ learning. However, existing literature has predominantly relied on visual approaches to analyze and compare the sequencing of textbook materials—a method that has been in use for over two decades—with few studies employing statistical methods. This reliance on visual methods highlights a noticeable lack of statistical analyses in this area. Without quantitative indicators, the relationship between textbook sequencing features and students’ academic performance cannot be effectively studied.To advance the field, this research included 31 Algebra textbooks used in 9th grade across the U.S. and coded their content, supporting and motivational materials. The content coding was based on the Mathematics Curriculum Document Analysis content framework published in 2022. The coding for supporting and motivational materials in the textbooks was primarily based on motivation theories, reasoning demands of today’s society on students, and indications of the use of the Common Core State Standards in the textbooks.Two approaches were developed: visual mapping and statistical measurement, to analyze and compare the sequencing of three major components (Mathematics Topics, Motivational Materials, Mathematics Reasoning) and content topics. Chapter 4 provides a detailed explanation of the methods used in this study. The visual mappings offer clear representations of how content topics and motivational materials are distributed throughout the textbooks, revealing varied patterns across different textbooks, as discussed in Chapter 5.However, these observations rely on visual inspection rather than quantitative analysis. Chapters 6 and 7 present the statistical measurements using Markov chain techniques and model-based approach, showcasing the sequencing features of the three major components and content topics separately. By doing so, the differences in textbook structures were measured through quantitative indicators, describing pattern similarities and differences in students’ learning opportunities.This study moves beyond graphical analysis, offering a deeper understanding of textbook organization. More importantly, it provides quantitative indicators for future data analysis, enabling researchers to explore the relationship between sequencing features and academic outcomes. Chapter 8 provides an example of how to use these indicators in practice. This research has the potential to inform educators, curriculum developers, and policymakers about the effective ways to organize textbook content to enhance learning opportunities and improve educational outcomes for students. A full discussion is presented in Chapter 9.
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