## 4 Result Analysis
### Experimental and Case Studies
\begin{table}
\begin{tabular}{l l} \hline \hline
**References** & **Title** \\ \hline
[113] & Can you Escape from [PERSON]’s Lab? Educational Escape Rooms with Scientists, \\  & Riddles and Serious Games as Learning Tools \\
[114] & Effect of Collaboration Mode and Position Arrangement on Immersive Analytics \\  & Tasks in Virtual Reality: A Pilot Study \\
[115] & A Distance Learning VR Technology Tool for Science Labs \\  & Learning Analytics Enabled Virtual Reality Content Creation Platform: System \\  & Design and Preliminary Evaluation \\  & Needs Analysis and Prototype Evaluation of Student-facing LA Dashboard for Virtual \\  & Reality Content Creation \\
[118] & The design and evaluation of an AR-based serious game to teach programming \\
[119] & Towards Multi-modal Evaluation of Eye-tracked Virtual Heritage Environment \\
[120] & A study on learning analytics of using mobile augmented reality application to \\  & enhance cultural competence for design cultural creation in higher education \\  & An Empirical Evaluation of Educational Data Mining Techniques in a Dynamic VR \\  & Application \\
[122] & Automated Analysis of Text in Student-created Virtual Reality Content \\  & Experience the Theory: New Perspectives Through VR Learning Environments for \\  & Photography Education \\  & Learning Analytics for Assessing Hands-on Laboratory Skills in Science Classrooms \\  & Using Bayesian Network Analysis \\  & Using Deep Learning to Track Representational Flexibility Development of Children \\  & with Autism in a Virtual World \\
[126] & Using learning analytics to investigate learning processes and behavioural patterns in \\  & an augmented reality language learning environment \\
[127] & A Platform for Analyzing Students’ Behavior in Virtual Spaces on Mozilla Hubs \\  & Applying multimodal data fusion to track autistic adolescents’ representational \\  & flexibility development during virtual reality-based training \\  & Approximating eye gaze with head pose in a virtual reality microteaching scenario for \\  & pre-service teachers \\  & In-game performance: The role of students’ socio-economic status, self-efficacy and \\  & situational interest in an augmented reality game \\
[131] & Unveiling Synchrony of Learners’ Multimodal Data in Collaborative Maker Activities \\
[132] & Utilizing augmented reality for embodied mental rotation training: A learning \\  & analytics study \\  & Learning Analytics for Collaboration Quality Assessment during Virtual Reality \\  & Content Creation \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._involved both students and teachers (5.55%), and two studies involved various participants (5.55%), as can be seen in Table 7. On average, each study involved 108 participants. Hence, the emphasis on students is highlighted. Furthermore, the technology and the method which the studies focused on were also explored. Most studies put an emphasis on virtual reality (n = 22, 61.1%) and, to a lesser extent, on augmented reality (n = 13, 36.1%). Only one study focused on mixed reality (2.8%), as can be seen in Table 8. Additionally, significantly more emphasis was put on learning analytics (n = 28, 77.8%) than educational data mining (n = 3, 8.3%). However, five studies (13.9%) explored both learning analytics and educational data mining. The related information is presented in Table 9.  
Table 10 presents the distribution of the systems used within the studies. Based on the outcomes, most studies focused on virtual learning environments (n = 17, 47.2%), followed by mobile applications (n = 9, 25.0%) and digital games (16.7%). Only 3 studies (8.3%) focused on platforms and a single study (2.8%) adopted a sandbox in their experiments. However, little information was given regarding the tools and approaches used to develop