Incorporating affect into conceptual change modeling for a computer-based scientific inquiry learning environment is difficult. The challenges mainly stemmed from three perspectives: first, to identify the appropriate variables of affect that influence conceptual change, second, to determine the causal dependencies between the variables of affect and the variables of conceptual change, third, to perform assessment on the evolving states of affect as a student interacts with computer-based learning activities. This research work employed Bayesian Networks as an attempt to tackle the challenges. Three Bayesian Network models of conceptual change were proposed and integrated into INQPRO, a educational program developed in this research work. The first model has only nodes of conceptual change, while the second and the third model have nodes of affect component. Two phases of empirical study were conducted involving a total of 143 students and the findings suggested that the third model that has nodes of affect had outperformed those models without them.