A large number of researchers have explored the use of non-parametric item response theory (IRT) models, including Mokken scale analysis (Mokken, 1971), for inspecting rating quality in the context of performance assessment. Unlike parametric IRT models, such as Many-Facet Rasch Model (Linacre, 1989), non-parametric IRT models do not entail logistic transformations of ordinal ratings into interval scales neither do they impose any constraints on the form of item response functions. A disregarded method for examining raters' scoring patterns is the non-parametric item characteristic curve estimation using kernel smoothing approach (Ramsay, 1991) which provides, without giving numerical values, graphical representations for identifying any unsystematic patterns across various levels of the latent trait. The purpose of this study is to use the non-parametric item characteristic curve estimation method for modeling and examining the scoring patterns of raters. To this end, the writing performance of 217 English as a foreign language (EFL) examinees were analyzed. The results of rater characteristic curves, tetrahedron simplex plots, QQ-plot, and kernel density functions across gender subgroups showed that different exploratory plots derived from the non-parametric estimation of item characteristic curves using kernel smoothing approach can identify various rater effects and provide valuable diagnostic information for examining rating quality and exploring rating patterns , although the interpretation of some graphs are subjective. The implications of the findings for rater training and monitoring are discussed.