## 4 Results
### Description of Artifact  
The VR artifact was created to integrate key elements into the teaching and learning of production sequencing. It was developed using Plant Simulation software to simulate dynamic systems, specifically a production system, through a VR application. The VR artifact utilized six different scenarios to model the production system, with each scenario adjusting input data and controlling output information [79]. The data input involved processing time per product, setup time, demand, production batch size, available capacity, production flow, and equipment availability indexes (maintenance and reliability). The system was designed to operate over 14 days, producing eight different products, each with its own specific data. We evaluated the quantities produced, demand fulfillment, cash flow generated, average and maximum production lead time, OEE (overall equipment efficiency), number of setups, and in-process stock levels for each scenario. The initial development of the artifact, including simulations and parameter adjustments, took 40 h. During the validation stage, each evaluator received individual presentations. The evaluators noted several positive aspects, including the impact of scenarios on evaluated indicators, the ability to visualize the production system's operation, the dynamics of inventory in the process, and the ease of adjusting scenario input data. Additionally, a list was created with six opportunities to modify parameters and the visual aspects of the VR environment. The opportunities included adjusting the production batch size parameter to observe the impact on in-process inventory levels and manufacturing lead time, highlighting the effect on cash flow by increasing work-in-process inventory levels, analyzing the quantity produced exceeding the expected demand for the 14-day simulation, entering a different color for each product in the VR model, incorporating conveyors to enhance material flow visualization in VR, and limiting the number of parts on the conveyor belt awaiting processing to five units to emphasize in-process stock in the buffers. After adjustments, the model was presented to all experts collectively, and all features were reassessed. The experts deemed the model suitable for implementation in achieving the proposed objective. Figure 2 displays the VR images of the production process. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Groups** & **Control** & **Experimental** \\ \hline \multirow{2}{*}{Gender} & Male & 19 & 25 \\ \cline{2-3}  & Female & 15 & 9 \\ \hline \multicolumn{3}{c}{The average age of the participants} & 28.9 years & 28.7 years \\ \hline \multicolumn{3}{c}{Previously familiar with the topic of} \\ \multicolumn{3}{c}{production sequencing} & 6 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characterization of the groups that participated in the experiment. Tables 4 and 5, which display the results of the \(t\)-test, suggest that the second alternative hypothesis, H\({}_{21}\), is supported based on the level of significance (\(p\)-value < 0.01). The results indicate a 51.8% increase in the EG and a 31.7% increase in the CG in the average knowledge acquired from the post-test compared to the pre-test. An additional analysis was carried out to determine whether any group showed greater improvement, and the findings are outlined in Table 6.  
Table 6 displays the result of the knowledge test administered after classes, with scores of 4.6229 and 5.9747 for the control group (CG) and experimental group (EG), respectively. The \(t\)-test yielded a \(p\)-value of 0.000012 (less than 0.01). The third alternative hypothesis, H\({}_{31}\), is supported. The EG group's result was 29.2% higher. EG, who had VR-supported classes, achieved more significant and statistically meaningful learning outcomes than the CG. Table 7 analyzes knowledge retention between the groups conducted three weeks after the initial knowledge tests.