## 3 Materials and Methods  
The study utilizes a laboratory experiment. Unlike a field experiment, a laboratory experiment operates on a smaller scale and controls a limited number of variables. In contrast, a field experiment would involve true magnitude and require a longer duration, potentially impacting academic records with potential consequences for students. The presence of real agents and their genuine responses distinguishes the laboratory experiment from an in silico experiment, which simulates agent behavior using software, which can lead to errors if the behaviors are not fully known or modeled. The advantage of a laboratory experiment is the ability to make corrections without real-world implications, but it may overlook certain variables and nuances present in a larger-scale experiment. Following the experiment, a mathematical analysis is required to validate results and test hypotheses. The EG and the CG were chosen for comparative analysis [76]. The study was structured into six stages, which are as follows:  
1. Definition of research objectives;
2. Development of the VR artifact;
3. Validation of the VR artifact with experts;
4. Application of the VR artifact in controlled groups;
5. Analysis and interpretation of results;
6. Conclusions and proposals for future research. The introduction describes the first step of the method and presents the cognitive approach to teaching. VR is also discussed, along with some examples of its application and results in learning, satisfaction, attractiveness, and student engagement. The second, third, and fourth steps reference the steps presented in Figure 1. Step 5 analyzed the results and discussed the literature on the topic. Finally, step 6 presented conclusions and proposals for future research. The steps presented in Figure 1 are described below:  
In Stage I, the model's assumptions were utilized to simulate production scenarios. Such assumptions involved adjusting various inputs such as processing times, production batches, setup times, MTBF, and MTTR. The production system output indicators, such as WIP levels, production lead time, quantities produced, and production costs, were then evaluated. In Stage II, the Plant Simulation software was utilized to model dynamic systems, allowing for the simulation of input changes and control of output indicators, as well as the generation of 3D VR images. Non-immersive VR was employed without direct interaction with students. Moving on to Stage III, the researchers presented and validated the VR model with three experts: two engineering professors and a market professional with over a decade of experience in production sequencing. This validation process involved a comprehensive evaluation of the model's input data, output indicators, and the visual and dynamic aspects of the VR environment. In Stage IV of the study, an experiment was conducted to assess the impact of a specific teaching method on student learning, satisfaction, engagement, and attractiveness. The sample comprised 68 students from the Production Engineering Course, with 34 students in each group--the control group (CG) and the experimental group (EG). The sample was defined in a non-probabilistic way for convenience [77]. The total student population in the course is 230. All students were invited to participate, and those who agreed formed the two groups based on availability. The control group received ten hours of traditional teaching. In comparison, the experimental group received six and a half hours of traditional teaching and an additional three and a half hours with VR support. Three weeks after the last session, a study was conducted to evaluate the knowledge retention level of each group [68]. Stages V, VI, and VII--three stages were applied for knowledge factual assessments, and four analyses were carried out. RQ1 is investigated by testing hypotheses H1, H2, and H3. RQ2 is investigated by the qualitative part of the questionnaire presented in Appendix B, which belongs to Stage VII of the method.