This study explores the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS), a neuro-fuzzy approach, to analyze the log data of technology-based assessments to extract relevant features of student problem-solving processes, and develop and refine a set of fuzzy logic rules that could be used to interpret student performance. The log data that record student response processes while solving a science simulation task were analyzed with ANFIS. Results indicate the ANFIS analysis could generate and refine a set of fuzzy rules that shed lights on the process of how students solve the simulation task. We conclude the article by discussing the advantages of combining human judgments with the learning capacity of ANFIS for log data analysis and outlining the limitations of the current study and areas of future research.