## 4 Result Analysis
### Theoretical and Review Studies
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Title** & **Year** \\ \hline
[67] & A Learning Analytics Theoretical Framework for STEM Education Virtual Reality & 2020 \\  & Applications & \\
[68] & Theoretical Foundations of Virtual and Augmented Reality-Supported Learning & 2020 \\  & Analytics & \\
[69] & Virtual Reality Application Integrated with Learning Analytics for Enhancing English & 2020 \\  & Pronunciation: A Conceptual Framework & 2020 \\  & What are the risks of Virtual Reality data? Learning Analytics, Algorithmic Bias and a & 2023 \\
[70] & Fantasy of Perfect Data & 2021 \\
[71] & ARLEAN: An Augmented Reality Learning Analytics Ethical Framework & 2021 \\
[72] & A Learning Analytics Conceptual Framework for Augmented Reality-Supported & 2021 \\  & Educational Case Studies & 2021 \\
[73] & On Top-Down Versus Bottom-up Personalisation and Evaluation of Augmented & 2021 \\  & Reality Learning Systems & 2021 \\
[74] & An Architecture for Capturing and Presenting Learning Outcomes using Augmented & 2022 \\  & Reality Enhanced Analytics & 2022 \\
[75] & A Modularity Approach in Design Science Research for Mixed Reality Learning & 2023 \\  & Artifacts & 2023 \\
[76] & Augmented Reality User’s Experience: AI-Based Data Collection, Processing and & 2023 \\  & Analysis & 2024 \\
[77] & Towards using the xAPI specification for Learning Analytics in Virtual Reality & 2024 \\
[78] & Virtual, augmented reality and learning analytics impact on learners, and educators: & 2024 \\  & A systematic review & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cont. xAPI for learning analytics. They reported that implementing learning analytics in virtual reality learning environments can yield several benefits; however, there are requirements and limitations that need to be considered. [PERSON] et al. [74] also explored how to capture, analyze, and present learning data that derived from augmented reality applications. In their work, they presented a system architecture for mobile devices and applications which consisted of various parts, such as data collection, aggregation, and storage, process metrics, analytics presentation, learning outcomes, and affective factors analytics. The ability to support teachers in offering personalized learning experiences to students through the use of such applications was highlighted. [PERSON] and [PERSON] [78] examined the use of learning analytics within augmented reality and virtual reality environments. Specifically, they reported that these technologies can support educators and increase students' performance. Finally, they commented upon the need for institutions and industrial organization to adopt these technologies. [PERSON] et al. [64] explored theoretical and practical frameworks regarding the use of educational data mining techniques and their use in mobile-based augmented reality applications. Their work highlighted that designing learning activities should follow the theories of learning and instruction and aimed at modeling and evaluating the impacts of design parameters on students' learning and engagement. [PERSON] et al. [66] presented an xAPI framework to combine learning analytics and augmented reality educational experiences. The study also presented key design considerations and investigated system requirements. Focusing on the use of learning analytics and educational data mining techniques, [PERSON] [73] examined top-down and bottom-up approaches for personalizing and evaluating augmented reality learning systems. The study also presented different methods of identifying students' preferences and explored different techniques to analyze the generated data to offer personalized learning. [PERSON] et al. [76] put an emphasis on the use of artificial intelligence tools to collect, process, and analyze learning data deriving from augmented reality applications.