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This report proposes an overview of Learning Analytics (LA) methods, benefits and challenges. The problem in Learning Analytics is that higher education institutions focus more on LA data evaluation and not on understanding its background. There is gap between an educator and LA implementation in educational process. With this literature review, I am trying to fill in this gap and give a background information of Learning Analytics for the potential higher education stakeholders.1. IntroductionWithin the past years, internet availability and technology have increased significantly which changed the entire world of information technology. This created the opportunity to track and store learners’ performances in a form of Big Data sets within online environments. Big data refers to capability of storing huge sets of data over a continued period and down to particular transactions (Picciano, 2014). As Brown (2012) defines, a systematic process of collecting and analysing large data sets from the online sources in order to improve learning processes is called Learning Analytics (LA), and it is a developing field in education. The experters in online learning are predicting that Learning Analytics will grow even more in the upcoming years. Learning analytics, academic analytics and educational data mining are related concepts (Chatti et al., 2012). Academic Analytics (AA) focuses on implementing actionable intelligence in order to improve teaching, learning and students accomplishments (Campbell et al., 2007). Most of the literature that talks about academic analytics are referring to mostly problematic students, that is, those students that might drop out of their programs. Education data mining (EDM) consists of developing methods that are for exploring the unique types of data that come from educational background. Those methods are used for a better understanding of students and conditions in which they learn (Romero et al., 2010). In this report I am using Learning Analytics term that cover all three concepts mentioned above. Siemens (2011) defines LA as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” (video file). Since LA is using so many different actions like process mining, data processing, technology-enhanced learning, education data mining, it is a field that is nowadays creating its own domain. Using Learning analytics can add a big value to learners and educators. Many higher education institutions are nowadays consisting of blended approach between a traditional classroom and self-regulated learning processes. LA can help students to improve their performances by reflecting on their learning process (Scheffel et al., 2014). Scheffel et al. (2014) refer to Scho?n (1983) who claims that once a student is aware of his learning situation, he reflects on the overall performance and “on the prior understandings which have been implicit in his behaviour”, which would engage him in the process of continuous learning. Similarly, if educators want to support their students, they need to understand of what students are doing, how are they interacting with the educational content, and where could potential problems occur (Scheffel et al., 2014). Despite the great value that Learning Analytics can bring to higher education institutions, there has been some criticism towards this concept. Online learning technologies nowadays can offer the opportunity of personalizing the learning environments based on the gathered datasets, and all this depends on managing the datasets of students, e.g. tracking their performances. This can create potential challenges, related to technical factors, data tracking and collection, ethical and privacy issues (Classroom-aid.com., 2013). Even though there has been some studies based on Learning Analytics in higher education institutions, it is still remaining as an emerging field. Since the value that LA adds to learners and educators is clearly identified (Long & Siemens, 2011), there is still a small amount of research that has been made on empirical LA studies, their methods and tools. The stakeholders included into the faculties need to be more familiar with LA methods (Scheffel et al., 2014). To be able to fill in this gap, I conducted a literature review based on Learning Analytics methods, benefits and challenges. I provided an overview of each part for the stakeholders that are not experts in LA but need to develop it, or have a basic understanding of this concept. Since LA is an emerging field, I believe that this kind of understanding is essential for every educational institution.