Learning analytics in the era of big data: A systematic literature review protocol

Abstract

Learning analytics is the art and science of collecting, analyzing and reporting data about learners and their learning environments in order to better understand and optimize the learning process and its environment. It is closely associated to educational data mining. Machine learning, data mining and human computer interaction techniques are used to the data collected about learners and their interactions. It aims at leveraging the learning process and learners' experience through the use of ubiquitous sensor based infrastructure, increased collection of large and diverse set of data, and subsequently applying machine learning and data analytics could really make it useful. Big data principles can be also very helpful to solve the mysteries related to the collection, storage management and analysis of large and heterogeneous set of data. Systematic Literature Review (SLR) is a more structured literature review process. It also provides more thorough coverage of the literature thereby minimizing the research bias. This paper aims at developing a systematic literature review protocol for the learning analytics to highlight the applications, issues and challenges, existing solutions and future directions in the context of big data.