N-Gain Normality Test

N-Gain Normality Test: For Strengthening Programs and Modules with One Group Pretest-Posttest provides a practical and methodological guide for educators, researchers, lecturers, and students who seek to evaluate the effectiveness of educational programs, instructional modules, and learning interventions using a one-group pretest-posttest design. The book introduces the fundamental concepts of pretest and posttest measurement, N-Gain calculation, gain-score interpretation, and normality testing. It explains how N-Gain can be used to examine changes in learning outcomes following an educational intervention and how normality analysis can support appropriate statistical decision-making. The discussion connects theoretical foundations with practical research procedures, enabling readers to understand not only how to calculate N-Gain but also how to interpret and report the results appropriately. Organized into ten chapters, the book covers the mathematical foundations of educational measurement, mastery learning and modular instruction, research design, N-Gain methodology and classification, research context and participants, instrument development, data analysis, interpretation of effectiveness, practical implications, and directions for future research. Particular attention is given to the methodological considerations involved in analyzing pretest-posttest data and strengthening the credibility of educational research findings. With its accessible structure and research-oriented approach, this book is designed as a useful reference for mathematics education, educational research, teacher education, curriculum development, and instructional program evaluation. It can also serve as a supplementary resource for researchers and postgraduate students who are conducting quantitative or quasi-experimental studies involving learning gains. By combining conceptual explanations with practical analytical guidance, this book aims to help readers make more informed interpretations of educational intervention data and communicate their findings clearly, systematically, and responsibly.