Innovative Pedagogical Models for Teaching R Software: SPS Approach and a Proposed Application

R is widely used for tasks such as statistical computations, data analysis, and visualization. As R software is open-source and free, its teaching is becoming increasingly popular in higher education. Recently, courses on R software have been incorporated into master's and doctoral programs. The Selective Problem Solving (SPS) model is a novel approach to creative problem-solving in mathematics education, based on mathematical problem-solving models, insightful thinking theories, and creativity research. It involves six sequential steps: defining the target problem, identifying the source problem, solving the target problem, creating an original problem, solving the original problem, and concluding with an evaluation. This study explores the theoretical framework of the SPS model and its application in teaching the R programming language. It specifically demonstrates the SPS model's use in selecting desired elements within matrices using R. The implementation is detailed through sub-steps and illustrative examples. Anticipated contributions include enriching content and fostering differentiation for educators teaching R. This study underscores the SPS model's viability as a teaching tool for R and suggests future exploration of its use for teaching various R-related subjects, as well as examining the overall efficacy of R instruction facilitated by the SPS model.