## 5 Discussion
The studies examined report positive outcomes in terms of learning gains, learning outcomes, students' motivation and engagement, and the provision of personalized learning. Additionally, this combination not only supports students in their learning but also aids teachers to more effectively teach, supervise, and mentor students. Due to its nature, it can be used to support different learning strategies and approaches, such as blended learning, personalized learning, and flipped classrooms while also supplementing teachers with real-time access to information relevant to students' engagement, interest, and performance [65; 115; 126]. These tools enable real-time identification, tracking, monitoring, and analysis of basic data and multimodal data of the learning process as well as of students' behavior, emotions, cognitive and affective states, and complex cognitive skill development [70; 75; 76; 108; 110; 125; 127; 128; 129], which, in turn, can lead to the development of effective student models [102; 111]. Machine learning and deep learning play a vital role in the realization of learning analytics and educational data mining [145; 146; 147]. These immersive and personalized environments enable experiential, hands-on learning [123; 124], offer authentic and realistic learning [65], and provide students with a sense of freedom [103]. Additionally, they promote and support social learning and collaborative learning in both co-located and distributed collaborative learning scenarios [100; 106; 114; 133; 131]. The provision of additional virtual information and feedback is viewed as interesting, understandable, and useful and supports learners, particularly those that are characterized as visual, sensor, and intuitive learners [73; 103; 119]. Due to these traits, students express positive attitudes toward these experiences [105]. Additionally, it can improve students understanding, performance, knowledge retention, and skills [112; 118; 120; 122; 123; 132], increase their confidence [69], enhance their learning motivation and engagement [107; 113], and promote students' self-efficacy [130] and self-regulated learning [117]. Hence, this combination of technologies and methods can create new learning opportunities in both formal and informal learning environments [98] and positively affect various subjects within an academic curriculum [101]. Additionally, this approach can effectively support special education [128] and enable the identification of errors in the learning design process [121]. Besides its ability to support students, this combination can also effectively support teachers [78], by automating mundane tasks [99], monitoring students' attention and focus [129], improving the evaluation and assessment processes [104], supporting learning and classroom management [109; 116], and enabling teachers to provide students with personalized feedback and guidance according to their learning needs [74]. Therefore, it can be inferred that the integration of learning analytics and educational data mining within augmented reality and virtual reality environments and their adoption in classrooms can result in more efficient teaching and learning.