To improve learning processes on online educational platforms, accurately modeling student knowledge is essential. A significant challenge arises when trying to measure knowledge across students who have attempted different subsets of questions. Traditional student learning models often rely on sequence prediction or factor analysis, typically using unidimensional parameters for students or questions. The Knowledge Tracing Machines (KTM) framework addresses these limitations by generalizing these models to higher dimensions (up to twenty). Additionally, KTM leverages side information by identifying the specific knowledge components involved in each question—often structured as a Q-matrix—to better capture complex learning interactions.