A review of ten years of implementation and research in aligning learning design with learning analytics at the Open University UK

There is an increased recognition that learning design drives both student learning experience and quality enhancements of teaching and learning. The Open University UK (OU) has been one of few institutions that have explicitly and systematically captured the designs for learning at a large scale. By applying advanced analytical techniques on large and fine-grained datasets, the OU has been unpacking the complexity of instructional practices, as well as providing conceptual and empirical evidence of how learning design influences student behaviour, satisfaction, and performance. This study discusses the implementation of learning design at the OU in the last ten years, and critically reviews empirical evidence from eight recent large-scale studies that have linked learning design with learning analytics. Four future research themes are identified to support future adoptions of learning design approaches

Unravelling the dynamics of learning design within and between disciplines in higher education using learning analytics

Designing effective learning experience in virtual learning environment (VLE) can be supported by learning analytics (LA) through explicit feedback on how learning design (LD) influences students’ engagement, satisfaction and performance. Marrying LA with LD not only puts existing pedagogical theories in instructional design to the test with actual learning data, but also provides the context of learning which helps educators translate established LA findings to direct interventions. My dissertation aims at unpacking the complexity of LD and its impact on students’ engagement, satisfaction and performance on VLE using LA. The context of this study is 400+ online and blended learning modules at the Open University (OU) UK. This research combines multiple sources of data from the OU Learning Design Initiative (OULDI), system log data, self-reported surveys, and performance data. Given the scope of this study, a wide range of visualization techniques, social network analysis, multi-level modelling, and machine learning will be used

The Immersive Education Laboratory: understanding affordances, structuring experiences, and creating constructivist, collaborative processes, in mixed-reality smart environments

In this paper we describe how the iClassroom and other technologies are providing the testbed through which we are able to design, develop, and research future intelligent environments. We describe the process of distinguishing between the technical and pedagogical aspects of immersive learning environments, while simultaneously considering both in the redefinition of effective intelligent learning spaces. This paper describes how our laboratory is working on specific projects that increase our understanding of the distinct advantages of technical design elements, like immersive visual displays, and pedagogical design elements that need to be in place as we go through the process of structuring learning situations that create constructivist, collaborative experiences. We describe specific technologies and their design across these multiple dimensions and the ways in which they are helping us better understand how to maximize technological affordances for increased positive learning outcomes. Finally, through this design research process, as we begin to better understand the affordances and iteratively create design guidelines, our hope is that eventually a prescriptive framework emerges that informs both the practice of embedded technology development and the deliberate incorporation of technical attributes into both the educational space and the pedagogy through which students learn

Mixing and Matching Learning Design and Learning Analytics

In the last five years, learning analytics has proved its potential in predicting academic performance based on trace data of learning activities. However, the role of pedagogical context in learning analytics has not been fully understood. To date, it has been difficult to quantify learning in a way that can be measured and compared. By coding the design of e-learning courses, this study demonstrates how learning design is being implemented on a large scale at the Open University UK, and how learning analytics could support as well as benefit from learning design. Building on our previous work, our analysis was conducted longitudinally on 23 undergraduate distance learning modules and their 40,083 students. The innovative aspect of this study is the availability of fine-grained learning design data at individual task level, which allows us to consider the connections between learning activities, and the media used to produce the activities. Using a combination of visualizations and social network analysis, our findings revealed a diversity in how learning activities were designed within and between disciplines as well as individual learning activities. By reflecting on the learning design in an explicit manner, educators are empowered to compare and contrast their design using their own institutional data

Mobile Learning: location, collaboration and scaffolding inquiry

Critiques of mobile learning pedagogy are concerned with whether such approaches are technology led. This chapter discusses how the particular features of mobile learning can be harnessed to provide new learning opportunities in relation to collaboration, inquiry and location-based learning. Technology supported inquiry learning is a situation rich with possibilities for collaboration. In particular, mobile learning offers new possibilities for scaffolding collaboration together with its other better-known features such as scaffolding the transfer between settings and making learning relevant by making use of the possibilities of location-based learning. These features are considered as part of mobile learning models, in particular mobile collaborative learning models

Exploring teachers’ needs and the existing barriers to the adoption of Learning Design methods and tools: a literature survey

Producción CientíficaLearning Design (LD) is oriented to support teachers in designing their teaching with the aim to provide a sound pedagogical background and to make effective use of resources and technologies. In spite of the significant number of LD approaches and tools proposed so far, their adoption is still very limited and this represents an unsolved challenge in the field of LD. This paper presents a systematic review of the literature about learning design tools, tackling the issue of adoption from two points of view: teachers’ needs in relation to LD tools and methods and possible barriers to their adoption. The review includes only research papers where teachers’ behaviours and opinions are directly explored and not purely theoretical papers. The search included five main academic databases in Technology Enhanced Learning (TEL) plus a search on Google about project reports; the resulting corpus included 423 papers: 26 of these, plus 3 reports were included in the final list for the analysis. The review provides a systematic overview of the knowledge developed in the LD field, focusing on a set of research gaps that need further exploration in the future.Ministerio de Economía, Industria y Competitividad (Projects TIN2014-53199-C3-2-R and TIN2017- 85179-C3-2-R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA082U16)European Commission (Proyect 588438-EPP-1-2017-1-EL-EPPKA2-KA

What would learning in an open world look like? A vision for the future

The pace of current technological advancement is phenomenal. In the last few years we have seen the emergence of ever more sophisticated gaming technologies, rich, immersive virtual worlds and new social networking services that enable learners and teachers to connect and communicate in new ways. The pace of change looks set to continue as annual Horizon reports testify (http://www.nmc.org/horizon). Clearly new technologies offer much in an educational context, with the promise of flexible, personalised and student-centred learning. Indeed research over the past few years, looking at learners' use of technologies, has given us a rich picture of how learners of all ages are appropriating new tools within their own context, mixing different applications for finding/managing information and for communicating with others (Sharpe and Beetham, forthcoming)

Models for Learning (Mod4L) Final Report: Representing Learning Designs

The Mod4L Models of Practice project is part of the JISC-funded Design for Learning Programme. It ran from 1 May – 31 December 2006. The philosophy underlying the project was that a general split is evident in the e-learning community between development of e-learning tools, services and standards, and research into how teachers can use these most effectively, and is impeding uptake of new tools and methods by teachers. To help overcome this barrier and bridge the gap, a need is felt for practitioner-focused resources which describe a range of learning designs and offer guidance on how these may be chosen and applied, how they can support effective practice in design for learning, and how they can support the development of effective tools, standards and systems with a learning design capability (see, for example, Griffiths and Blat 2005, JISC 2006). Practice models, it was suggested, were such a resource. The aim of the project was to: develop a range of practice models that could be used by practitioners in real life contexts and have a high impact on improving teaching and learning practice. We worked with two definitions of practice models. Practice models are: 1. generic approaches to the structuring and orchestration of learning activities. They express elements of pedagogic principle and allow practitioners to make informed choices (JISC 2006) However, however effective a learning design may be, it can only be shared with others through a representation. The issue of representation of learning designs is, then, central to the concept of sharing and reuse at the heart of JISC’s Design for Learning programme. Thus practice models should be both representations of effective practice, and effective representations of practice. Hence we arrived at the project working definition of practice models as: 2. Common, but decontextualised, learning designs that are represented in a way that is usable by practitioners (teachers, managers, etc).(Mod4L working definition, Falconer & Littlejohn 2006). A learning design is defined as the outcome of the process of designing, planning and orchestrating learning activities as part of a learning session or programme (JISC 2006). Practice models have many potential uses: they describe a range of learning designs that are found to be effective, and offer guidance on their use; they support sharing, reuse and adaptation of learning designs by teachers, and also the development of tools, standards and systems for planning, editing and running the designs. The project took a practitioner-centred approach, working in close collaboration with a focus group of 12 teachers recruited across a range of disciplines and from both FE and HE. Focus group members are listed in Appendix 1. Information was gathered from the focus group through two face to face workshops, and through their contributions to discussions on the project wiki. This was supplemented by an activity at a JISC pedagogy experts meeting in October 2006, and a part workshop at ALT-C in September 2006. The project interim report of August 2006 contained the outcomes of the first workshop (Falconer and Littlejohn, 2006). The current report refines the discussion of issues of representing learning designs for sharing and reuse evidenced in the interim report and highlights problems with the concept of practice models (section 2), characterises the requirements teachers have of effective representations (section 3), evaluates a number of types of representation against these requirements (section 4), explores the more technically focused role of sequencing representations and controlled vocabularies (sections 5 & 6), documents some generic learning designs (section 8.2) and suggests ways forward for bridging the gap between teachers and developers (section 2.6). All quotations are taken from the Mod4L wiki unless otherwise stated