Eye tracking enables the reconstruction of eye movements and thus the analysis of visual information selection and integration processes during problem solving. In this way, learner-specific difficulties can be identified and problem-solving process can be adapted accordingly. For such an adaptation, the prediction of response behavior plays a crucial role. To predict whether a problem is solved correctly or incorrectly, the segmentation of the visual stimulus into specific areas of interest (AOIs) is particularly crucial for the quality of a prediction based on eye-tracking data. In the study presented here, the gaze data of N = 115 students were analyzed while solving the Test of Understanding Graphs in Kinematics (TUG-K), a validated test instrument whose items include graphs of position, velocity, and acceleration versus time. For selected items, response accuracy was predicted based on visual attention using multiple logistic regression analysis, examining the influence of AOI segmentation. The prediction quality could be significantly improved when the diagram was not considered as contiguous AOI, but when it was divided into solution-relevant and solution-irrelevant areas. To verify that the AOIs selected by the regression algorithm are indeed relevant to the solution process, an expert rating was performed, which showed moderate to good agreement between the AOIs rated by the experts as relevant to the correct solution and the AOIs selected by the algorithm. There are also pairs of items in the TUG-K that require the same mathematical solution procedure but differ in the physical context. This opened the possibility to investigate a new approach. Based on response accuracy and allocation of visual attention to one item, the response accuracy of the other item of the pair was predicted. It could be shown that the prediction quality based on visual attention was significantly higher than the prediction based on response accuracy.