## 4 Result Analysis
### Analysis of the Document Collection
#### 4.1.6 Analysis of the Technologies and Methods Used in the Studies  
Furthermore, since this study investigates the utilization of extended reality technologies and their combination with educational data mining and learning analytics, the frequency of the documents that put an emphasis on these technologies and methods was explored. Based on Figure 8, it can be stated that most studies focused on virtual reality technology (n = 43, 61.4%), followed by augmented reality technology (n = 27, 38.6%). Only a few studies (n = 3, 4.3%) examined using educational data mining and learning analytics in mixed reality settings. Thes outcomes are in line with the results of previous studies, which showcase that although still at its early stages, virtual reality is more mature and more widely examined when compared to augmented reality and mixed reality. Another reason that has led to virtual reality being more widely studied is the related equipment (e.g., virtual reality head-mounted devices) that are more advanced and developed in comparison to augmented reality devices, as well as the fact that developing fully virtual environments is more streamlined than the development of mixed reality environments. However, given the fact that the integration of augmented reality has showcased positive learning outcomes when integrated into classrooms and given the advances in the field, it is expected that more emphasis will also be placed on augmented reality in the future. It should be noted that 2 studies examined both augmented reality and virtual reality and 1 study focused on both mixed reality and virtual reality. Hence, the total document number presented in Figure 8 adds up to 73 and not to 70 which is the total number of documents.  
According to the findings presented in Figure 9, most studies put an emphasis on exploring using learning analytics within extended reality environments (n = 66, 94.29%), while only 11 studies (15.71%) focused on using educational data mining. It should be mentioned that seven studies explored using both educational data mining and learning  
Figure 8: Distribution of studies based on the technology used.  
analytics. Hence, the total number of documents presented in Figure 9 adds up to 77 and not to 70, which is the number of documents examined. Based on the findings, it can be inferred that due to the data generated through extended reality experiences utilizing learning analytics is more widely and frequently used to provide meaningful inputs to both teachers and students. However, more recent studies have also started to focus on capitalizing on using educational data mining to create more adaptive and personalized learning experiences by trying to investigate the meaning behind the raw data. Another reason that has led to this significant change in the number of studies that focus on learning analytics and educational data mining is the fact that studies mostly utilized raw data deriving from the applications and/or platforms used to examine students' performance and actions and did not apply more advanced techniques to identify patterns, create effective predictive models, and develop more efficient recommendation systems. Additionally, based on the outcomes of Figure 9, it is evident that more emphasis should be put on examining the implications of educational data mining within extended reality environments to capitalize on the multimodal data generated within them.