Innovating for Learning: Designing for the Future of Education

Teaching has moved online as the world has moved online and learning is losing its sense of physical location with the availability of many different options from mobile to MOOC (Massive Open Online Course). The impact of online learning is not confined to distance learning; when a student attends a campus university they are now as likely to meet with their fellow learners virtually as face to face. The education sector has yet to fully adapt to what this means, and indeed there strong signs of a built in resilience from providers, employers and students themselves which may mean an apparent evolution is more likely than a revolution. At the same time, there are some quiet changes underway that mean we should be preparing to innovate for the revolution to come. Some of those changes are considered in work undertaken at The Open University that has been disseminated in a series of Innovating Pedagogy reports. These reports allow the academic authors to be more speculative than is usual practice and engage in considering the future, while remaining based on a view of what is happening in the sector. In particular they adopt a position focused on pedagogy that balances technology-based futurology that can dominate yet fail to resonate with those actually involved in the teaching process. The annual Innovating Pedagogy reports cover 10 topics each, with some deliberate overlap from year to year and development of themes that show innovations moving into teaching practice. This is illustrated by two cases, the impact of MOOCs and the application of learning design and analytics. The development of MOOCs demonstrates the value of reviewing pedagogy that aligns with technology. While the use of learning design and learning analytics demonstrates how improvements in the way we describe our learning processes and the way we understand learner behaviour is helping determine how choices in pedagogy impact on student satisfaction, progression and success

CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

Incorporating Online Instruction in Academic Libraries: Getting Ahead of the Curve

A sea change in higher education is shaping the way many libraries deliver instruction to their students and faculty. Years of technological innovation and changes in the way that people discover and use information has made online instruction an essential part of a library\u27s teaching and learning program. In order to evaluate our library\u27s online instruction program and to determine its future goals, we analyzed the technology, pedagogical models, organizational structures, administrative supports, and partnerships we would need in order to succeed. Our findings may be useful for libraries reassessing their own online instruction programs

Understanding Communication Patterns in MOOCs: Combining Data Mining and qualitative methods

Massive Open Online Courses (MOOCs) offer unprecedented opportunities to learn at scale. Within a few years, the phenomenon of crowd-based learning has gained enormous popularity with millions of learners across the globe participating in courses ranging from Popular Music to Astrophysics. They have captured the imaginations of many, attracting significant media attention - with The New York Times naming 2012 "The Year of the MOOC." For those engaged in learning analytics and educational data mining, MOOCs have provided an exciting opportunity to develop innovative methodologies that harness big data in education.Comment: Preprint of a chapter to appear in "Data Mining and Learning Analytics: Applications in Educational Research

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

Innovating Language Education: An NMC Horizon Project Strategic Brief

The NMC is a leading educational technology organization. A main outcome of the collaboration between the Language Flagship Technology Innovation Center and the NMC was this publication, which highlights recommendations that emerged from discussions of major trends, challenges, and technology developments by experts and practitioners in language technologies in higher education. Innovating Language Education identifies main trends and areas of interest and constitutes a rich resource that includes key definitions and proofs of concept

Improving Online Education Using Big Data Technologies

In a world in full digital transformation, where new information and communication technologies are constantly evolving, the current challenge of Computing Environments for Human Learning (CEHL) is to search the right way to integrate and harness the power of these technologies. In fact, these environments face many challenges, especially the increased demand for learning, the huge growth in the number of learners, the heterogeneity of available resources as well as the problems related to the complexity of intensive processing and real-time analysis of data produced by e-learning systems, which goes beyond the limits of traditional infrastructures and relational database management systems. This chapter presents a number of solutions dedicated to CEHL around the two big paradigms, namely cloud computing and Big Data. The first part of this work is dedicated to the presentation of an approach to integrate both emerging technologies of the big data ecosystem and on-demand services of the cloud in the e-learning field. It aims to enrich and enhance the quality of e-learning platforms relying on the services provided by the cloud accessible via the internet. It introduces distributed storage and parallel computing of Big Data in order to provide robust solutions to the requirements of intensive processing, predictive analysis, and massive storage of learning data. To do this, a methodology is presented and applied which describes the integration process. In addition, this chapter also addresses the deployment of a distributed e-learning architecture combining several recent tools of the Big Data and based on a strategy of data decentralization and the parallelization of the treatments on a cluster of nodes. Finally, this article aims to develop a Big Data solution for online learning platforms based on LMS Moodle. A course recommendation system has been designed and implemented relying on machine learning techniques, to help the learner select the most relevant learning resources according to their interests through the analysis of learning traces. The realization of this system is done using the learning data collected from the ESTenLigne platform and Spark Framework deployed on Hadoop infrastructure

MASELTOV Deliverable Report 7.2: Feedback and Progress Indicators

This document explores the range of feedback and progress indicators (FPIs) that can be used to support incidental, mobile learning for the target MASELTOV audience, recent immigrants to the EU. We propose that feedback, and progress indicators (we differentiate between the two) should play an instrumental role in helping learners reflect upon individual, often isolated learning episodes mediated by single MASELTOV services, and enable them to reconceive them as constituting elements of a coherent, larger learning journey. The goal of feedback and progress indicators is to support the motivation for learning and from this the social inclusion of recent immigrants. Our underpinning assumption is that the MASELTOV software designers’ goal should be to encourage not just resolution of immediate challenges (e.g. finding a doctor, translating a sign) but a user’s reflection on their continuing progress towards integration into the host country, including improving their language skills. We define feedback as responses to a learner’s performance against criteria of quality and as a means of directing and encouraging the learner; and progress indicators as responses indicating the current position of a learner within a larger activity or journey (often related to time). Drawing partly from the worlds of web-based language learning and video games, we identify which feedback and progress indicators may best support incidental mobile learning, and the major challenges faced. For some MASELTOV services, feedback and progress indicators for large scale learning journeys are less apparent (e.g. TextLens, the MASELTOV tool that enables a user to take a photo of a sign and convert the image into text, potentially for future viewing or translation), while some services are explicitly educational (e.g. language lessons). However we see all of these as potentially part of an ecology of services that can support social inclusion, so all tools should include FPIs that encourage broader learning goals. In this document we draw on the Common European Framework of Reference for Languages as appropriate, and also reflect on learner perspectives (derived from WP2 and WP9 findings) to identify suitable FPIs, as well as being informed by academic literature. Furthermore, we recommend FPIs that would be suitable for the MASELTOV tools and services. The remainder of the deliverable handles the four identified key areas where mobile incidental learning particularly requires FPIs: 1. encouraging reflection 2. future goal setting 3. planning 4. social learning It should be noted that this document is a high level review, identifying significant literature and key examples of FPIs in practice. This document offers recommendations therefore in general terms. Decisions about specific FPIs to be implemented will be made in coordination with technical partners to identify which MASELTOV services and tools will support which specific feedback and progress indicators, and how they will be implemented within the system