For testlet response data, traditional item response theory (IRT) models are often not appropriate due to local dependence presented among items within a common testlet. Several testlet-based IRT models have been developed to model examinees' responses. In this paper, a new two-parameter normal ogive testlet response theory (2PNOTRT) model for dichotomous items is proposed by introducing testlet discrimination parameters. A Bayesian model parameter estimation approach via a data augmentation scheme is developed. Simulations are conducted to evaluate the performance of the proposed 2PNOTRT model. The results indicated that the estimation of item parameters is satisfactory overall from the viewpoint of convergence. Finally, the proposed 2PNOTRT model is applied to a set of real testlet data.