In this talk, we study several advanced approaches to predict student performance on parameterized questions. The approaches we use are Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), Feature-Aware Student Knowledge Tracing (FAST), Bayesian Probabilistic Tensor Factorization (BPTF) and Bayesian Probabilistic Matrix Factorization (BPMF) (the last two from the recommender system’s field). We approach the problem using two dimensions: domain knowledge structure and students’ attempt time sequence. We use both topic-level and question-level Knowledge Components (KCs) for domain knowledge structure and test the methods on a dataset of parameterized questions. Our experiments show that, when having only the knowledge-item-level information, all of the models work similarly in predicting student performance, but adding the topic-level information that integrates knowledge items changes the performance of these models in different directions. Also, our studies show the importance of considering time sequence of students’ attempts to achieve the desirable accuracy.