This module guides students in the use of differential equation models to predict cancer growth and study treatment outcomes. Several classical models for cancer growth are presented including exponential, power law, Bertalanffy, logistic, and Gompertz. Students solve first-order differential equations using guess and check. They examine the behaviors of the equations and solutions through qualitative techniques. Furthermore, students evaluate how cancer treatments affect tumor growth. Real cancer data are provided to give options for data fitting and prediction of cancer growth. It addresses some main topics in differential equations, such as initial value problems, analytic solutions, qualitative approaches, and numerical approximation. Through step-by-step investigation, conjecturing, predicting, and analyzing, students discover how their knowledge can be used to address complex and real problems, and improve problem solving ability and mathematical reasoning skills.
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