Traditional methods evaluating a potential treatment have focused on the average treatment effect. However, there exist situations where individuals can experience significantly heterogeneous responses to a treatment. In these situations, one needs to account for the differences among individuals when estimating the treatment effect. Li et al. (2022)  proposed a method based on random forest of interaction trees (RFIT) for a binary or categorical treatment variable, while incorporating the propensity score in the construction of random forest. Motivated by the need to evaluate the effect of tutoring sessions at the Math and Stat Learning Center (MSLC), we extend their approach to an ordinal treatment variable. Our approach improves upon the RFIT for multiple treatment by incorporating the ordered structure of the treatment variable into the tree growing process. To illustrate the effectiveness of our proposed method we will conduct simulation studies where the results show our proposed method has a lower mean squared error, higher optimal treatment classification, and is able to identify the most important variables that impact the treatment effect. We then apply the proposed method to student success data in order to estimate how the number of visits impacts the probability of a student passing an introductory statistics course. Our results show that every student is recommended to go to the MSLC at least once and some can drastically improve their chance of passing the course by going the optimal number of times. Furthermore, we introduce the CERFIT R package which is an implementation of RFIT in the R programming language. We also implement the integration of C++ via the Rcpp package and are able to greatly improve the computation speed.