## 3 Results and Discussion  
### Descriptive Statistics  
After the necessary data are collected to test the performance of a newly developed tool/module, the first step is to analyze these data to investigate any patterns that can be found. Our survey instrument was comprised of six demographic questions and five background questions. The goal behind the use of descriptive statistics is to gain an insight into the sample distribution as well as general information about the various features of the sample structure. Figure 14 presents the participants' demographic information distributions, and Figure 15 shows their background information distributions. The background information involves six items that can be rated on a scale from zero (novice) to four (expert). Figure 14: Demographics information (**a**) Participants’ gender distribution. (**b**) Participants’ ethnicity distribution. (**c**) Participants’ current education level. (**d**) Participants’ field of study distribution. (**e**) Participants’ nationality distribution. (**f**) Participants’ age category distribution. Figure 14a presents the participants' gender distribution. The vertical axis scale depicts the number of participants according to both genders, male and female, and indicates that more males participated in the study (63%). Figure 14b presents the participants' ethnicity distribution. There are four main ethnicity categories: Asian, Black/AA, Hispanic/Latino, and White. As shown in the figure, the majority of the participants were white. In fact, around 63% participants were white. Figure 14c presents the education level in which the participating students were enrolled at the time of the study. As illustrated, most students were working towards a bachelor's degree. In fact, 46% of participants were pursuing a bachelor's degree, 26% a master's degree, and 26% a PhD degree. Figure 14d presents the participants' field of study distribution. All participants belonged to one of the following two major fields: Engineering and Business Information Systems (BIS). Engineering included four subfields: Aerospace Engineering (ASE), Chemical Engineering (CE), Industrial Engineering (IE), and Mechanical Engineering (ME). As illustrated in the figure, most participants were majoring in Industrial Engineering (53%). Figure 14e presents the participants' nationality distribution. A total of 60% were domestic students. Figure 14f presents the participants' age category distribution. Most participants were between 21 and 25 years old. In fact, 43% of the participants were between 21 and 25 years old. Figure 15: Background information (**a**) Participants’ VR technology knowledge distribution. (**b**) Participants’ video game-playing experience distribution. (**c**) Participants’ coding experience distribution. (**d**) Participants’ R programming knowledge distribution. (**e**) Participants’ grammar of graphics theory knowledge distribution. (**f**) Participants’ analytics knowledge distribution. Figure 15a shows the participants VR technology knowledge distribution. As illustrated, the majority of participants had basic VR system knowledge. Only 6% of the participants had above average VR knowledge, and none were recorded as experts in this area. Figure 15b presents the participants' video game-playing experience distribution. Most participants did not play video games often, and only 30% recorded their game-playing experience to be above-average/expert. Figure 15c shows the participants' coding experience distribution. Most participants had basic coding experience and only 30% reported having above average coding experience. Figure 15d shows the participants' R programming knowledge distribution. Most participants reported expert R programming software knowledge--40% reported having R programming language knowledge at the above-average and expert levels, while only 23% reported having no R programming knowledge. Figure 15e shows the participants' grammar of graphics theory knowledge distribution.