Taking a conceptual idea and turning it into a precise algorithm is at the heart of computational thinking. However, novice programmers often struggle when their code does not behave as they intended. This paper identifies problems in pseudocode algorithms based on deviations from observations that could be gathered during manual simulations by the programmer. By applying model-based diagnosis to the faulty pseudocode, informed by manual simulation, we locate errors and suggest fixes. The diagnosis results in the identification of a specific location in the algorithm and provides an output description for the faulty part that matches the programmer's intent during the manual simulation, thus aiding in debugging.