Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

The future of technology enhanced active learning – a roadmap

The notion of active learning refers to the active involvement of learner in the learning process, capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learnercontent interaction, is a central aspect of technology-enhanced active learning. In this roadmap, the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap

Using mobile technology to create flexible learning contexts

This paper discusses the importance of learning context with a particular focus upon the educational application of mobile technologies. We suggest that one way to understand a learning context is to perceive it as a Learner Centric Ecology of Resources. These resources can be deployed variously but with a concern to promote and support different kinds of mediations, including those of the teacher and learner. Our approach is informed by sociocultural theory and is used to construct a framework for the evaluation of learning experiences that encompass various combinations of technologies, people, spaces and knowledge. The usefulness of the framework is tested through two case studies that evaluate a range of learning contexts in which mobile technologies are used to support learning. We identify the benefits and challenges that arise when introducing technology across multiple locations. An analytical technique mapped from the Ecology of Resources framework is presented and used to identify the ways in which different technologies can require learners to adopt particular roles and means of communication. We illustrate how we involve participants in the analysis of their context and highlight the extent to which apparently similar contexts vary in ways that are significant for learners. The use of the Ecology of Resources framework to evaluate a range of learning contexts has demonstrated that technology can be used to provide continuity across locations: the appropriate contextualization of activities across school and home contexts, for example. It has also provided evidence to support the use of technology to identify ways in which resources can be adapted to meet the needs of a learner