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Logistic Knowledge Tracing (LKT) is a framework for combining various predictive features into student models that are adaptive, interpretable, explainable, and accurate. While the name logistic knowledge tracing was coined for our R package that implements this methodology for making student models, logistic knowledge tracing originates with much older models such as Item Response Theory (IRT), the Additive Factors Model (AFM), and Perfor-mance Factors Analysis (PFA), which exemplify a type of model where student performance is represented by the sum of multiple components each with some sort of feature computed for the component. Features may range from the simple presence or ab-sence of the component to complex functions of the prior history of the component. The LKT package provides a simple interface to this methodology, allowing old models to be specified or new models to be created by mixing and matching components with features. We will provide concrete examples of how the LKT framework can provide interpretable results on real-world datasets while being highly accurate.