This study utilized a hierarchical Bayesian joint modeling approach to concurrently analyze item responses with the Rasch model and response time (RT) data with a lognormal model in a statistical learning task. Further, different models of RT including the Boxcox, Exponential, and Gamma RT distributions were empirically compared. Response accuracy (RA) based on the Rasch-Only model was compared with that based on joint models. The parameter estimation for all models was performed using Markov chain Monte Carlo methods. The results indicated that the Rasch-Boxcox model was the best-fitting joint model. The correlation between the item difficulty and item speed parameters as well as the correlation between the person ability and person speed parameters were both negative in the Rasch-Boxcox joint models, which led to smaller standard errors in both item difficulty and ability parameter estimates in the joint modeling compared to the Rasch-Only model, indicating the auxiliary information from RT helps improve the measurement precision of RA.