## 1 Introduction  
There is an ever increasing need to integrate data science into educational contexts to further enhance learning and teaching [1]. Via educational data science, educational processes can be tailored to each individual's requirements, needs, and preferences [2]. Among the various methods and approaches used in the domain of data science in education, learning analytics and educational data mining are the most widely used [3,4]. Educational data hold a lot of value and meaning [5] and can be used in many ways, including analyzing students' performance [6]. Therefore, and due to the potential benefits that they can bring in the educational domain, research into educational data mining [7] and learning analytics [8] is continuously increasing. It should be noted that despite their sharing similarities and their being applied at all educational levels, there are distinct differences between the two [2,9]. Specifically, learning analytics evaluate the educational process and learning theories using existing models in a top-down approach, while educational data mining strives to create new models and identify new patterns in data in a bottom-up approach [10,11]. Additionally, there are several other differences regarding the methods, adaptations, techniques, personalization strategies, origins, and approaches used between the two [12,13]. As a result, it can be inferred that learning analytics put an emphasis on addressing educational challenges, while educational data mining puts an emphasis on the technical ones [14]. Specifically, learning analytics is an interdisciplinary scientific field that is increasing in popularity since learning analytics can enrich current education models [12,14]. Learning analytics focuses on analyzing data, commonly deriving from educational environments, to enhance the overall quality of teaching and learning, to improve personalization, and to overcome existing barriers and challenges [15,16]. Hence, learning analytics involves the processes of collecting, processing, analyzing, and visualizing data to better understand teaching and learning processes to further improve educational experiences and environments [3,13,14,17,18,18]. Learning analytics can support both teachers and students, help meet their needs, and positively influence teachers' performance and students' progress [19,20]. Hence, learning analytics can be utilized to comprehend and optimize the educational process and the environment in which it occurs [21]. Several systematic literature reviews have examined the role and use of learning analytics in educational settings [21, 22, 23, 24, 25, 26]. These studies highlight the positive effect that they can have on learning and teaching practices, point out the educational outcomes that can be yielded, and comment upon the fact that this area of study is still in its infancy. Another fact that derives from these studies is the drastic need to further explore how multi-modal data can be used in the context of learning analytics to increase learning outcomes. Simultaneously, educational data mining is also gaining ground [3]. Based on the \"International Educational Data Mining Society\", educational data mining is \"an emerging discipline, concerned with developing methods for exploring the unique and increasingly large-scale data that come from educational settings and using those methods to better understand students, and the settings which they learn in\" [27]. Hence, it constitutes an interdisciplinary study field that capitalizes on statistics, machine learning, deep learning, information retrieval, and artificial intelligence [5]. Specifically, this specialized data mining form aims to process information, analyze data, develop models, and identify patterns within data to address educational challenges, improve institutional effectiveness, and enhance learning outcomes and experiences [2,11,28].