Learning analytics is a significant area of technology-enhanced learning that has emerged during the last decade. This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learningfocused perspectives and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining and academic analytics. Finally, it examines developing areas of learning analytics research, and identifies a series of future challenges.Keywords: academic analytics; action analytics; educational data mining; learning analytics; social learning analytics. Biographical notes:Rebecca Ferguson is a full-time Research Fellow in the UK Open University's Institute of Educational Technology, where her work is focused on how people learn together online. She works as a research lead on the SocialLearn team, developing and researching initiatives to improve pedagogical understanding of learning in online settings and to design analytics to support the assessment of learning in these settings.

The design of effective learning analytics extends beyond sound technical and pedagogical principles. If these analytics are to be adopted and used successfully to support learning and teaching, their design process needs to take into account a range of human factors, including why and how they will be used. In this editorial, we introduce principles of human-centred design developed in other, related fields that can be adopted and adapted to support the development of Human-Centred Learning Analytics (HCLA). We draw on the papers in this special section, together with the wider literature, to define human-centred design in the field of learning analytics and to identify the benefits and challenges that this approach offers. We conclude by suggesting that HCLA will enable the community to achieve more impact, more quickly, with tools that are fit for purpose and a pleasure to use.

This special issue deals with three areas. Learning design is the practice of devising effective learning experiences aimed at achieving defined educational objectives in a given context. Teacher inquiry is an approach to professional development and capacity building in education in which teachers study their own and their peers' practice. Learning analytics use data about learners and their contexts to understand and optimise learning and the environments in which it takes place. Typically, these three-design, inquiry and analytics-are seen as separate areas of practice and research. In this issue, we show that the three can work together to form a virtuous circle. Within this circle, learning analytics offers a powerful set of tools for teacher inquiry, feeding back into improved learning design. Learning design provides a semantic structure for analytics, whereas teacher inquiry defines meaningful questions to analyse.

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Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior, 76 pp. 703-714. For guidance on citations see FAQs.

In this paper, the authors examine the state of the art in augmented reality (AR) for mobile learning. Previous work in the field of mobile learning has included AR as a component of a wider toolkit but little has been done to discuss the phenomenon in detail or to examine in a balanced fashion its potential for learning, identifying both positive and negative aspects. The authors seek to provide a working definition of AR and to examine how it can be embedded within situated learning in outdoor settings. The authors classify it according to key aspects (device/technology, mode of interaction/learning design, type of media, personal or shared experiences, whether the experience is portable or static, and the learning activities/outcomes). The authors discuss the technical and pedagogical challenges presented by AR, before looking at ways in which it can be used for learning. Finally, the paper looks ahead to AR technologies that may be employed in the future.

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Personalisation of learning is a recurring trend in our society, referred to in government speeches, popular media, conference and research papers and technological innovations. This latter aspect-of using personalisation in technology-enhanced learning (TEL)-has promised much but has not always lived up to the claims made. Personalisation is often perceived to be a positive phenomenon, but it is often difficult to know how to implement it effectively within educational technology.In order to address this problem, we propose a framework for the analysis and creation of personalised TEL. This article outlines and explains this framework with examples from a series of case studies. The framework serves as a valuable resource in order to change or consolidate existing practice and suggests design guidelines for effective implementations of future personalised TEL. IntroductionPersonalization is a key topic of current interest in technology-oriented learning design and discussion for government policy makers, but less so in educational research. This paper develops a framework to support the design of technology-enhanced learning (TEL) resources and environment.