Rebecca Ferguson

Learning analytics: drivers, developments and challenges

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 learning-focused 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.

Social learning analytics: five approaches

This paper proposes that Social Learning Analytics (SLA) can be usefully thought of as a subset of learning analytics approaches. SLA focuses on how learners build knowledge together in their cultural and social settings. In the context of online social learning, it takes into account both formal and informal educational environments, including networks and communities. The paper introduces the broad rationale for SLA by reviewing some of the key drivers that make social learning so important today. Five forms of SLA are identified, including those which are inherently social, and others which have social dimensions. The paper goes on to describe early work towards implementing these analytics on SocialLearn, an online learning space in use at the UKs Open University, and the challenges that this is raising. This work takes an iterative approach to analytics, encouraging learners to respond to and help to shape not only the analytics but also their associated recommendations.

Augmented Reality and Mobile Learning: The State of the Art

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.

Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs)

Massive open online courses (MOOCs) are now being used across the world to provide millions of learners with access to education. Many learners complete these courses successfully, or to their own satisfaction, but the high numbers who do not finish remain a subject of concern for platform providers and educators. In 2013, a team from Stanford University analysed engagement patterns on three MOOCs run on the Coursera platform. They found four distinct patterns of engagement that emerged from MOOCs based on videos and assessments. However, not all platforms take this approach to learning design. Courses on the FutureLearn platform are underpinned by a social-constructivist pedagogy, which includes discussion as an important element. In this paper, we analyse engagement patterns on four FutureLearn MOOCs and find that only two clusters identified previously apply in this case. Instead, we see seven distinct patterns of engagement: Samplers, Strong Starters, Returners, Mid-way Dropouts, Nearly There, Late Completers and Keen Completers. This suggests that patterns of engagement in these massive learning environments are influenced by decisions about pedagogy. We also make some observations about approaches to clustering in this context.

Innovative Pedagogy at Massive Scale: Teaching and Learning in MOOCs

This paper looks at the implications for pedagogy of education at a massive scale. It begins by looking at educational approaches designed or adapted to be effective for large numbers of learners: direct instruction, networked learning, connectivism, supported open learning, and conversational learning at scale. It goes on to identify benefits and challenges of teaching and learning at scale. A grounded approach was used to analyse data from 18 MOOCs run on the UK-based FutureLearn platform. This identified benefits and challenges for learners, for educators and for society as a whole. These need to be addressed in two ways, through learning design and through platform design.

Visualizing social learning ties by type and topic: rationale and concept demonstrator

Social Learning Analytics (SLA) are designed to support students learning through social networks, and reflective practitioners engage in informal learning through a community of practice. This short paper reports work in progress to develop SLA motivated specifically by Networked Learning Theory, drawing on the related concepts and tools of Social Network Analytics and Social Capital Theory, which provide complementary perspectives onto the structure and content of such networks. We propose that SLA based on these perspectives needs to devise models and visualizations capable of showing not only the usual SNA metrics, but the types of social tie forged between actors, and topic-specific subnetworks. We describe a technical implementation demonstrating this approach, which extends the Network Awareness Tool by automatically populating it with data from a social learning platform SocialLearn. The result is the ability to visualize relationships between people who interact around the same topics.

An evaluation of learning analytics to identify exploratory dialogue in online discussions

Social learning analytics are concerned with the process of knowledge construction as learners build knowledge together in their social and cultural environments. One of the most important tools employed during this process is language. In this paper we take exploratory dialogue, a joint form of co-reasoning, to be an external indicator that learning is taking place. Using techniques developed within the field of computational linguistics, we build on previous work using cue phrases to identify exploratory dialogue within online discussion. Automatic detection of this type of dialogue is framed as a binary classification task that labels each contribution to an online discussion as exploratory or non-exploratory. We describe the development of a self-training framework that employs discourse features and topical features for classification by integrating both cue-phrase matching and k-nearest neighbour classification. Experiments with a corpus constructed from the archive of a two-day online conference show that our proposed framework outperforms other approaches. A classifier developed using the self-training framework is able to make useful distinctions between the learning dialogue taking place at different times within an online conference as well as between the contributions of individual participants.

Exploring qualitative analytics for e-mentoring relationships building in an online social learning environment

The language of mentoring has become established within the workplace and has gained ground within education. As work based education moves online so we see an increased use of what is termed e-mentoring. In this paper we explore some of the challenges of forming and supporting mentoring relationships virtually, and we explore the solutions afforded by online social learning and Web 2.0. Based on a conceptualization of learning network theory derived from the literature and the qualitative learning analytics, we propose that an e-mentoring relationships is mediated by a connection with or through a person or learning objects. We provide an example to illustrate how this might work.

Moving Through MOOCS: Pedagogy, Learning Design and Patterns of Engagement

Massive open online courses (MOOCs) are part of the lifelong learning experience of people worldwide. Many of these learners participate fully. However, the high levels of dropout on most of these courses are a cause for concern. Previous studies have suggested that there are patterns of engagement within MOOCs that vary according to the pedagogy employed. The current paper builds on this work and examines MOOCs from different providers that have been offered on the FutureLearn platform. A cluster analysis of these MOOCs shows that engagement patterns are related to pedagogy and course duration. Learners did not work through a three-week MOOC in the same ways that learners work through the first three weeks of an eight-week MOOC.

Distributed cognition in a virtual world

Over a 13-month period, the Schome Park Programme operated the first ‘closed’ (i.e. protected) Teen Second Life 1 project in Europe. The project organised diverse educational events that centred on use of a virtual world and an associated asynchronous forum and wiki. Students and staff together exploited the affordances of the environment to develop skills and enhance community spirit. One popular activity, initiated by students, involved sailing boats around the projects virtual island, a technically challenging task for beginners. This paper studies the records of one of these sailing regattas. Organising and implementing this event involved considerable technical and interactional challenges. We analyse the following: How do people work together, including through the use of (virtual) artefacts, to solve problems? What particular qualities of the literacy practices surrounding the regatta appear to us to involve learning? Simultaneously, we contribute to the development of methodologies for studying learning in virtual worlds by employing a virtual literacy ethnography. Findings include a diversity of creative approaches that are used when solving problems, the significance of adult behaviour in authentically modelling learning and the value of humour in fostering a learning community. The notion of distributed cognition has implications for characterising learning and analytical approaches to analysis.