The analysis of data collected from the interaction of users with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new research field, learning analytics, and its closely related discipline, educational data mining. This paper first introduces the field of learning analytics and outlines the lessons learned from well-known case studies in the research literature. The paper then identifies the critical topics that require immediate research attention for learning analytics to make a sustainable impact on the research and practice of learning and teaching. The paper concludes by discussing a growing set of issues that if unaddressed, could impede the future maturation of the field. The paper stresses that learning analytics are about learning. As such, the computational aspects of learning analytics must be well integrated within the existing educational research.

He is working on research projects exploring how technology can be used to understand and influence human behavior. He has experience in the use of digital devices in areas such as behavioral analytics, social networks, computer-supported collaboration, personalization and technology-enhanced learning. George Siemens is the Executive AbstractThe massive adoption of technology in learning processes comes with an equally large capacity to track learners. Learning analytics aims at using the collected information to understand and improve the quality of a learning experience. The privacy and ethical issues that emerge in this context are tightly interconnected with other aspects such as trust, accountability and transparency. In this paper, a set of principles is identified to narrow the scope of the discussion and point to pragmatic approaches to help design and research learning experiences where important ethical and privacy issues are considered. Introduction: privacy in learning environmentsThe use of information and communication technology has significantly changed how learning experiences are conceived and deployed. The widespread use of various digital devices together with cloud computing allows for learning scenarios not previously considered. Students are now able to access a myriad of learning resources, interact with applications focusing on a specific topic, enhance their experience in virtual environments, augment reality and connect with others through social networks. The progress of technology evolves together with the capacity to record the events occurring in a learning environment. Every interaction and resource accessed can be captured and stored. As a consequence, learning scenarios can now be analyzed using big-data analytics techniques. Although the use of new technology is shaping the way we learn, a more significant change may derive from the use of big-data analytics (Siemens & Long, 2011).

Recently, learning analytics (LA) has drawn the attention of academics, researchers, and administrators. This interest is motivated by the need to better understand teaching, learning, “intelligent content,” and personalization and adaptation. While still in the early stages of research and implementation, several organizations (Society for Learning Analytics Research and the International Educational Data Mining Society) have formed to foster a research community around the role of data analytics in education. This article considers the research fields that have contributed technologies and methodologies to the development of learning analytics, analytics models, the importance of increasing analytics capabilities in organizations, and models for deploying analytics in educational settings. The challenges facing LA as a field are also reviewed, particularly regarding the need to increase the scope of data capture so that the complexity of the learning process can be more accurately reflected in analysis. Privacy and data ownership will become increasingly important for all participants in analytics projects. The current legal system is immature in relation to privacy and ethics concerns in analytics. The article concludes by arguing that LA has sufficiently developed, through conferences, journals, summer institutes, and research labs, to be considered an emerging research field.

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