Search Results for « Learning analytics »

The field of learning analytics has the potential to enable higher education institutions to increase their understanding of their students’ learning needs and to use that understanding to positively influence student learning and progression. Analysis of data relating to students and their engagement with their learning is the foundation of this process. There is an inherent assumption linked to learning analytics that knowledge of a learner’s behavior is advantageous for the individual, instructor, and educational provider. It seems intuitively obvious that a greater understanding of a student cohort and the learning designs and interventions they best respond to would benefit students and, in turn, the institution’s retention and success rate. Yet collection of data and their use face a number of ethical challenges, including location and interpretation of data; informed consent, privacy, and deidentification of data; and classification and management of data. Approaches taken to understand the opportunities and ethical challenges of learning analytics necessarily depend on many ideological assumptions and epistemologies. This article proposes a sociocritical perspective on the use of learning analytics. Such an approach highlights the role of power, the impact of surveillance, the need for transparency, and an acknowledgment that student identity is a transient, temporal, and context-bound construct. Each of these affects the scope and definition of learning analytics’ ethical use. We propose six principles as a framework for considerations to guide higher education institutions to address ethical issues in learning analytics and challenges in context-dependent and appropriate ways.

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 UK's 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.

2017 The Handbook of Learning Analytics Editors: Charles Lang, George Siemens, Alyssa Wise, Dragan Gašević ISBN: 978-0-9952408-0-3 DOI: 10.18608/hla17 Download Book Order printed copy The Handbook of Learning Analytics is designed to meet the needs of a new and growing field. It aims to balance rigor, quality, open access and breadth of appeal and wasContinue reading →

This article introduces learning analytics dashboards that visualize learning traces for learners and teachers. We present a conceptual framework that helps to analyze learning analytics applications for these kinds of users. We then present our own work in this area and compare with 15 related dashboard applications for learning. Most evaluations evaluate only part of our conceptual framework and do not assess whether dashboards contribute to behavior change or new understanding, probably also because such assessment requires longitudinal studies.

This article summarizes some emerging concerns as learning analytics become implemented throughout education. The article takes a sociotechnical perspective — positioning learning analytics as shaped by a range of social, cultural, political, and economic factors. In this manner, various concerns are outlined regarding the propensity of learning analytics to entrench and deepen the status quo, disempower and disenfranchise vulnerable groups, and further subjugate public education to the profit-led machinations of the burgeoning “data economy.” In light of these charges, the article briefly considers some possible areas of change. These include the design of analytics applications that are more open and accessible, that offer genuine control and oversight to users, and that better reflect students’ lived reality. The article also considers ways of rethinking the political economy of the learning analytics industry. Above all, learning analytics researchers need to begin talking more openly about the values and politics of data-driven analytics technologies as they are implemented along mass lines throughout school and university contexts.

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.

Learning Analytics focuses on the collection and analysis of learners' data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students' interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries). The dataset is freely available at https://analyse.kmi.open.ac.uk/open_dataset under a CC-BY 4.0 license.

Four descriptions of learning analytics are widely cited.Siemens (2010) described learning analytics as and analysis models to discover information and socialThe website for the 1 st International Conference on Learning Analytics and Knowledge posted this de-1 Educause models to predict student progress and performance, modelling capacity of analytics: to predict behaviour, act on predictions, and then feed those results back into the process in order to improve the predictions These descriptions beg fundamental questions.What data should be gathered for input to methods that 1 https:/ /tekri.athabascau.ca/analytics/computations that underlie analytics and, second, what analytics can say about phenomena.For instance, ordinal (rank) data preclude using arithmetic operations on data, such as addition or division.If data are not ordinal, A cannot be described as greater than B, nor are transitive statements valid: if A > B and B > C, then A > C. What properties of data bear on the validity of interventions based on learning analytics developed from grounds for intervening without other data.None of these data classes are legitimately considered a with other courses or a job).And, notably, prediction does not supply valid grounds for inferring causality.Learning ecologies involve multiple actors.Authors of learners about how to study; font styles and formats Chapter 21: Learning Analytics for Self-Regulated Learning

Learning analytics can improve learning practice by transforming the ways we support learning processes. This study is based on the analysis of 252 papers on learning analytics in higher education published between 2012 and 2018. The main research question is: What is the current scientific knowledge about the application of learning analytics in higher education? The focus is on research approaches, methods and the evidence for learning analytics. The evidence was examined in relation to four earlier validated propositions: whether learning analytics i) improve learning outcomes , ii) support learning and teaching , iii) are deployed widely, and iv) are used ethically. The results demonstrate that overall there is little evidence that shows improvements in students' learning outcomes (9%) as well as learning support and teaching (35%). Similarly, little evidence was found for the third (6%) and the forth (18%) proposition. Despite the fact that the identified potential for improving learner practice is high, we cannot currently see much transfer of the suggested potential into higher educational practice over the years. However, the analysis of the existing evidence for learning analytics indicates that there is a shift towards a deeper understanding of students’ learning experiences for the last years.