Tensor Factorization for Student Modeling and Performance Prediction in Unstructured Domain

We propose a novel tensor factorization approach, Feedback-Driven Tensor Factorization (FDTF), for modeling student learning process and predicting student performance. This approach decomposes a tensor that is built upon students' attempt sequence, while considering the quizzes students select to work with as its feedback. FDTF does not require any prior domain knowledge, such as learning resource skills, concept maps, or Q-matrices. The proposed approach differs significantly from other tensor factorization approaches, as it explicitly models the learning progress of students while interacting with the learning resources. We compare our approach to other state-of-the-art approaches in the task of Predicting Student Performance (PSP). Our experiments show that FDTF performs significantly better compared to baseline methods, including Bayesian Knowledge Tracing and a state-of-the-art tensor factorization approach. [For the full proceedings, see ED592609.]