## 4 Result Analysis
### Experimental and Case Studies
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Education Level** & **Freq.** & **Perc.** & **References** \\ \hline Primary Education & 4 & 11.1\% & [100,106,110,116] \\ Secondary & 6 & 16.6\% & [98,109,124,125,128,130] \\ Education & 24 & 66.7\% & [99,101–104,107,108,111–115,117–120,122,123,126,127,129,131–133] \\ Included various & 2 & 5.6\% & [105,121] \\ participants & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Education-level distribution. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Participants** & **Freq.** & **Perc.** & **References** \\ \hline Students & 32 & 88.9\% & [98,100–104,106–115,117–120,122–133] \\ Students and & 2 & 5.6\% & [99,116] \\ Fachers & 2 & 5.6\% & [105,121] \\ Various & 2 & 5.6\% & [105,121] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Participant distribution. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Technology** & **Freq.** & **Perc.** & **References** \\ \hline Virtual reality & 22 & 61.1\% & [102–108,111,114–117,119,121–123,125,127–129,131,133] \\ Augmented reality & 13 & 36.1\% & [98,100,101,109,110,112,113,118,120,124,126,130,132] \\ Mixed reality & 1 & 2.8\% & [99] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Technology distribution. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Freq.** & **Perc.** & **References** \\ \hline Learning analytics & 28 & 77.8\% & [98–100,103–109,111–120,123,126,127,129–133] \\ Learning analytics and & 5 & 13.9\% & [101,110,122,124,128] \\ Educational data mining & 3 & 8.3\% & [102,121,125] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Method distribution. the specific systems as the vast majority of studies did not report the platform or the tools they used during the development process. Of the ones that did, Unity arose as the most popular development platform [105; 115; 121; 123; 129], followed by Mozilla Hubs [127]. Similarly, in the case of educational data mining, only a few studies specified the techniques used. It should also be noted that a lack of information regarding machine learning and deep learning approaches was observed in the documents examined. Among the ones that did, machine learning and deep learning techniques, models, and approaches such as Bayesian networks [102; 121; 124], DBSCAN algorithm [110], supervised machine learning using multilayer perceptron (MLP) [125], and support vector machines (SVM), decision trees, and random forest under multimodal and unimodal approaches [128] were used. Lastly, a lack of information was observed regarding the equipment used during the experiment process. Of the 36 studies, only six (16.67%) reported the exact equipment used. Meta Quest 2 (n = 3) [121; 123; 129], Oculus Rift (n = 3) [103; 104; 114], and Microsoft Hololens (n = 2) [105; 108] were mostly used. Focusing on the research approaches adopted by the studies, the research methods used, the variables examined, and the experimental designs were examined. Specifically, and given the nature of the data generated, most studies adopted a quantitative research methodology (n = 27, 75.0%), while only a single study (2.8%) followed a qualitative research methodology. A total of eight studies (22.2%) adopted a mixed-method research approach. The related information is presented in Table 11. Although some studies focused on the collection and analysis of log files as well as application, system, or game-related data (50.0%), other studies also used other data collection tools (e.g., surveys, questionnaires, interview, observations, etc.) (50.0%). Most of the studies that used additional data collection tools opted for ad hoc tools. However, few studies also integrated existing tools, such as those presented in [134; 135; 136; 137; 138]. Table 12 presents the related data.