Nowadays, how to encourage students' reflective thinking is one of the main concerns for teachers at various educational levels. Many students have difficulties when facing tasks that involve high levels of reflection, such as on STEM (Science, Technology, Engineering and Mathematics) courses. Many also have deep-rooted anxiety and demotivation towards such courses. In order to overcome these cognitive and affective challenges, researchers have suggested the use of "Problem-Solving before Instruction" (PS-I) approaches. PS-I consists of giving students the opportunity to generate individual solutions to problems that are later solved in class. These solutions are compared with the canonical solution in the following phase of instruction, together with the presentation of the lesson content. It has been suggested that with this approach students can increase their conceptual understanding, transfer their learning to different tasks and contexts, become more aware of the gaps in their knowledge, and generate a personal construct of previous knowledge that can help maintain their motivation. Despite the advantages, this approach has been criticized, as students might spend a lot of time on aimless trial and error during the initial phase of solution generation or they may even feel frustrated in this process, which might be detrimental to future learning. More importantly, there is little research about how pre-existing student characteristics can help them to benefit (or not) from this approach. The aim of the current study is to present the design and implementation of the PS-I approach applied to statistics learning in undergraduate students, as well as a methodological approach used to evaluate its efficacy considering students' pre-existing differences.