In this study, we proposed an Online Item Response Theory Model (OIRT) by combining the Item Response Theory and Performance Factor Analysis (PFA) models. We fitted the proposed model with modified Variational Inference (VI) to perform real-time student and item parameter estimation using both simulated data and real time series data collected from an online adaptive learning environment. Results showed that modified VI parameter estimation method outperformed other Bayesian parameter estimation methods in efficiency and accuracy. We also demonstrated that OIRT tracked students' ability growth dynamically and efficiently, it also predicted students' future performance with reasonable AUC given limited input features.