Changepoints are abrupt variations in a sequence of data in statistical inference. In educational and psychological assessments, it is pivotal to properly differentiate examinees' aberrant behaviors from solution behavior to ensure test reliability and validity. In this paper, we propose a sequential Bayesian changepoint detection algorithm to monitor the locations of changepoints for response times in real time and, subsequently, further identify the types of aberrant behaviors in conjunction with response patterns. Two simulation studies were conducted to investigate the efficiency and accuracy of the proposed detection procedure in terms of identifying one or multiple change points at different locations. In addition to manipulating the number and locations of changepoints, two types of aberrant behaviors were also considered: rapid guessing behavior and cheating behavior. Simulation results indicate that ability estimates could be improved after removing responses from aberrant behaviors identified by our approach. Two empirical examples were analyzed to illustrate the application of proposed sequential Bayesian changepoint detection procedure. [This paper will be published in "British Journal of Mathematical and Statistical Psychology."]