## 3 Results and Discussion
### Effectiveness Analysis
#### 3.2.3 R and VR: A Comparison and Validation of the Modules  
All students managed to answer the tutorial tasks to visualize data, merge data, subset data, and answer the remaining knowledge questions correctly in both R and VR modules. Hence, no statistical analysis was required to compare the answers to the questions in both conditions. This also means that the IVEE engine is a tool that does what it is supposed to do in relation to the tasks it currently accommodates. The time to complete tasks in R and VR modules was also compared between the student participants who had good to expert R knowledge (experts) and those who had no to average R knowledge (novice) for all tasks (refer to the tables below). Table 5 presents the average total completion time in both R and VR environments for both categories (novice and expert student participants). The results show that it took experts 7 min and 51 s to complete the entire tutorial in R and 8 min 12 s to complete it in VR. The 21 differences between both study conditions were not significant, and experts managed to spend less time performing tasks in R because they were familiar with the software. However, it took them relatively more time to complete the tutorial tasks using IVEE because this was their first time using this VR module. This relatively insignificant difference in how long it took for experts to complete the tasks in R and IVEE indicates that even during their first time using this tool, they achieved good performance results in relation to timing. On the other hand, novice students took more time to complete the entire tutorial in R (29 min and 9 s). When total time was compared, novice participants managed to complete the tutorial in VR in approximately half the time it took them to complete it in R (the average VR completion time for novice students was 15 min and 9 s). These results are promising and show the promise of these new technologies for facilitating the work of analysts and reducing the time it takes them to perform analytics tasks. When comparing the average total time, it took novice students 15 min and 9s and expert students 8 min 12 s to complete the tasks in VR. The difference was only around 7 min--in contrast to R, where the difference between the average total completion time of novice (29 min and 9 s) and expert (7 min and 51 s) students was 22 min. This indicates that the VR environment managed to reduce the total time difference between both categories by approximately 15 min. These results are encouraging for the IA field and show that building new immersive, intuitive tools for analysts can reduce the time it takes them to complete these tasks even without any prior software knowledge. The decision to reduce the cognitive load on users by facilitating the use of IVEE through direct manipulation of the elements helped reduce the time it took for participants with no/basic/average knowledge in R to perform basic analytics tasks. Table 6 presents the average time it took students to generate visualizations in R and VR environments. The results show that novice and expert students took relatively less time to generate visualizations in VR (a 2 s difference between R and VR for experts and a 23 s difference between R and VR for novice students). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dependent** & **Sample** & **Mean** & **Standard** & & \\
**Variable** & **Size** & **(min)** & **Deviation (min)** & _df_ & _t-Value_ & _p-Value_ \\ \hline R Total Time & 30 & 18:26 & 08:02 & & & \\ \hline VR Total Time & 30 & 12:07 & 04:03 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of hypothesis testing: Paired _t_-test. Table 7 presents the average time it took participants to subset data using R and VR environments. The results show that experts took relatively more time to perform the subset task in VR (4 s difference) as opposed to novices who took less time to subset data using VR (1 min difference). Table 8 illustrates the average time it took students to merge data using R and VR environments.