## 1 Introduction
Due to the nature and scope of educational data mining, several systematic literature reviews have been conducted to examine its use in educational contexts [5,22,28,29,30]. The outcomes of these studies highlight the capabilities of educational data mining to enrich learning and teaching and comment upon the increasing need to capitalize on educational data and explore how to effectively identify and extract meaning from it. Through extended reality technologies, valuable data can be generated which can reveal additional key aspects when examined within educational settings. Specifically,extended reality refers to new reality formats that are provided through mixed reality, virtual reality, and augmented reality technologies [31]. Augmented reality involves users' physical environment enrichment with digital objects and virtual information that are interactive and can be perceived through users' senses [32; 33; 34]. Virtual reality involves fully virtual environments that perceptually surround users and simulate their physical presence within them [35; 36; 37] to create \"all-inclusive, sensory illusion of being present in another environment\" [38]. These environments provide high immersion, involvement, immediacy, and interaction [39; 40; 41; 42] and capitalize on psychological and social aspects to be perceived by users as real [43]. In the context of the \"reality-virtuality continuum\", somewhere in the middle between the real and virtual environments lies a mixed reality in which real and virtual contexts co-exist [44]. Within this continuum, virtual reality is nearer to the virtual environment, whereas augmented reality is nearer to the physical world [44]. Owing to their interactivity and immersiveness, these technologies are more widely utilized in educational settings across levels and subjects. The outcomes of systematic literature reviews that have examined both theoretical and experimental studies and focused on both augmented reality [45; 46; 47; 48] and virtual reality [49; 50; 51; 52] technologies have highlighted the benefits and the positive influence that their integration into the educational domain can bring about. Additionally, extended reality technologies can be combined with different approaches, such as gamification, and other technologies (e.g., artificial intelligence) to further increase learning outcomes and offer personalized learning experiences [53]. Due to their unique nature, additional data to that generated within traditional classrooms or when using traditional teaching means can be generated. By examining this data meaningful insights that can further increase our understanding of learning could be yielded. According to the aforementioned information, it is evident that by combining these technologies and methods, personalized learning environments that are characterized by high engagement and immersiveness can be created. The relationship among extended reality technologies, learning analytics, and educational data mining is presented in Figure 1. Specifically, through extended reality technologies, interactive, secure, and immersive learning environments that foster experiential learning can be created, which, in turn, can render the collection of multimodal data feasible. Using advanced algorithms and computational techniques in the context of educational data mining, the educational data can be analyzed to predict learners' behaviors, discover patterns, create predictive models, and offer meaningful recommendations to improve teaching and learning. Additionally, through learning analytics, the educational data can be systematically collected, processed, analyzed, visualized, and interpreted to optimize the overall educational process. However, despite the existing studies that have focused on these topics, there has not been any study, to the best of our knowledge, which explores how learning analytics and educational data mining are used within extended reality environments.