System upgrade: realising the vision for UK education

A report summarising the findings of the TEL programme in the wider context of technology-enhanced learning and offering recommendations for future strategy in the area was launched on 13th June at the House of Lords to a group of policymakers, technologists and practitioners chaired by Lord Knight. The report – a major outcome of the programme – is written by TEL director Professor Richard Noss and a team of experts in various fields of technology-enhanced learning. The report features the programme’s 12 recommendations for using technology-enhanced learning to upgrade UK education

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

A MOOC taxonomy based on classification schemes of MOOCs

In recent years there has been a significant growth in the number of online courses known as MOOCs available via online providers such as edX and Coursera. The result has been a marked reduction in the clarity around the different course offerings and this has created a need to reconsider the classification schemes for MOOCs to help inform potential participants. Many classifications have been proposed which cover the needs of academics and providers but may not be suitable for learners choosing a course. In this paper, the various classifications used by MOOC providers and aggregator services to categorise MOOCs in presenting information to prospective learners are gathered and analysed. As a result, 13 different categories are identified, which cover information provided to learners before entering a course. These categories are then compared and combined with classifications from the literature to create a taxonomy centred round eight terms: Massive (e.g. enrolments), Open (e.g. pre-requisites), Online (e.g. Timings), Assessment, Pedagogy (e.g. instructor-led), Quality (e.g. reviews), Delivery (e.g. educators), Subject (e.g. Syllabus). Thus, producing a taxonomy capable of categorising MOOCs from a wider perspective

Report to HEFCE on student engagement

This study, commissioned by the Higher Education Funding Council for England (HEFCE), explored the extent and nature of student engagement in the higher education sector in England. The study was concerned with institutional and student union processes and practices – such as those relating to student representation and student feedback – which seek to inform and enhance the collective student learning experience, as distinct from specific teaching, learning and assessment activities that are designed to enhance individual students’ engagement with their own learning. The study found that institutions view student engagement as central to enhancing the student experience, but the emphasis seems to be placed on viewing students as consumers. For student unions, the emphasis is on viewing them as partners in a learning community. The latter notion seems to be stronger in certain subject areas (for example, Art and Design and Performing Arts) than others. The majority of HEIs and FE colleges rate their student engagement processes – comprising a basic model of student feedback questionnaires and student representation systems – as reasonably or very effective; student unions are less likely to do so. Detailed discussions with staff and students within a diverse range of HE providers showed that actual practices vary between and within institutions and that their effectiveness could be improved. Higher education institutions, student unions and further education colleges with significant higher education provision were surveyed to establish a baseline measure of the nature and extent of student engagement processes. Further exploration of institutions’ formal and informal processes and their effectiveness was undertaken through fieldwork with a number of higher education providers and student unions

Innovating Pedagogy 2017: Exploring new forms of teaching, learning and assessment, to guide educators and policy makers. Open University Innovation Report 6

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This sixth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Learning In a NetworKed Society (LINKS) Israeli Center of Research Excellence (I-CORE). Themes: • Big-data inquiry: thinking with data • Learners making science • Navigating post-truth societies • Immersive learning • Learning with internal values • Student-led analytics • Intergroup empathy • Humanistic knowledge-building communities • Open Textbooks • Spaced Learnin

Emotions used in Learning Analytics: a state-of-the-art review

Emotions play a critical role in the learning and teaching process because learners’ feelings impact motivation, self-regulation and academic achievement. In this literature review of 100+ studies, we identify approximately 100 different emotions that may have a positive, negative or neutral impact on learners’ attitudes, behaviour and cognition. In this review, we explore seven methods of data gathering approaches to measure and understand emotions (i.e., content analysis, natural language processing, behavioural indicators, quantitative instruments, qualitative approaches, well-being word clouds, and intelligent tutoring systems). With increased affordances of technologies to continuously measure emotions (e.g., facial and voice expressions with tablets and smart phones), it might become feasible to monitor learners’ emotions on a real-time basis in the near future

The Educational Intelligent Economy – Lifelong Learning – A vision for the future

Almost every detail of our lives, where we go, what we do, and with whom is captured as digital data. Technological advancements in cloud computing, artificial intelligence, and data analytics offer the education sector new ways not only to improve policy and processes but also to personalize learning and teaching practice. However, these changes raise fundamental questions around who owns the data, how it might be used, and the consequences of use. The application of Big Data in education can be directed toward a wide range of stakeholders, such as educators, students, policy-makers, institutions, or researchers. It may also have different objectives, such as monitoring, student support, prediction, assessment, feedback, and personalization. This chapter presents the nuances and recent research trends spurred by technological advancements that ave influenced the education sector and highlights the need to look beyond the technical boundaries using a socio-semiotic lens. With the explosion of available information and digital technologies pervading cultural, social, political as well as economic spaces, being a lifelong learner is pivotal for success. However, technology on its own is not sufficient to drive this change. For technology to be successful, it should complement individual learning cultures and education systems. This chapter is broadly divided into two main sections. In the first section, we contemplate a vision for the future, which is deemed possible based on ongoing digital and computing advancements. The second section elaborates the technological, pedagogical, cultural, and political requirements to attain that vision

Design and Implementation of Scientific Inquiry using Technology in a Teacher Education Program

Two hundred and fifteen pre-service teachers engaged in a scientific inquiry unit in the newly created Bachelorof Primary and Early Childhood Education course at an Australian university This paper discusses how theTechnological, Pedagogical and Content Knowledge (TPACK) model provided the conceptual framework todesign an online inquiry unit. The unit enabled students to research an authentic problem focusing onenvironmental sustainability using an inquiry framework and an array of information and communicationtechnology (ICT) tools. The survey data collected at the conclusion of the unit indicated that 90 % of studentsthought the unit improved their understanding of the inquiry process and 88% reported more confidence in theirunderstanding of science concepts. Ninety four percent of students reported an increase in their knowledge andconfidence of Web 2.0 tools in supporting scientific inquiry in science. The research determined that the onlinescaffolded inquiry improved students’ knowledge and confidence in the skills and processes associated withinquiry and in science concepts. It will, however, not replace more traditional hands-on investigative approachesbut provides a complementary valuable tool to teach interesting and engaging science