Model tracing and constraint-based modeling are two approaches to diagnose student input in stepwise tasks. Model tracing supports identifying consecutive problem-solving steps taken by a student, whereas constraint-based modeling supports student input diagnosis even when several steps are combined into one step. We propose an approach that merges both paradigms. By defining constraints as properties that a student input has in common with a step of a strategy, it is possible to provide a diagnosis when a student deviates from a strategy even when the student combines several steps. In this study we explore the design of a system for multistep strategy diagnoses, and evaluate these diagnoses. As a proof of concept, we generate diagnoses for an existing dataset containing steps students take when solving quadratic equations \(\left( {n = 2136} \right)\) . To compare with human diagnoses, two teachers coded a random sample of deviations \(\left( {n = 70} \right)\) and applications of the strategy \(\left( {n = 70} \right)\) . Results show that that the system diagnosis aligned with the teacher coding in all of the 140 student steps.