Content-based VLE designs improve learning efficiency in constructivist statistics education

Background: We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific-purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. Objectives: The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Methods: Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. Results: The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a content-based design outperforms the traditional VLE-based design. © 2011 Wessa et al.

New lecturers' beliefs about learning, teaching and assessment in higher education:the role of the PGCLTHE programme

This study was carried out with new lecturers on a two year Post Graduate Certificate in Learning and Teaching in Higher Education programme in a UK university. The aim was to establish their beliefs about how studying on the programme aligned with their teaching and learning philosophy and what, if anything, had changed or constrained those beliefs. Ten lecturers took part in an in-depth semi-structured interview. Content analysis of the transcripts suggested positive reactions to the programme but lecturers’ new insights were sometimes constrained by departments and university bureaucracy, particularly in the area of assessment. The conflicting roles of research and teaching were also a major issue facing these new professionals.

Technology enhanced learning in Higher Education

Technology-Enhanced Learning in Higher Education is an anthology produced by the international association, Learning in Higher Education (LiHE). LiHE, whose scope includes the activities of colleges, universities and other institutions of higher education, has been one of the leading organisations supporting a shift in the education process from a transmission-based philosophy to a student-centred, learning-based approach. Traditionally education has been envisaged as a process in which the teacher disseminates knowledge and information to the student, and directs them to perform – instructing, cajoling, encouraging them as appropriate – despite different students’ abilities. Yet higher education is currently experiencing rapid transformation, with the introduction of a broad range of technologies which have the potential to enhance student learning. This anthology draws upon the experiences of those practitioners who have been pioneering new applications of technology in higher education, highlighting not only the technologies themselves but also the impact which they have had on student learning. The anthology illustrates how new technologies – which are increasingly well-known and accepted by today’s ‘digital natives’ undertaking higher education – can be adopted and incorporated. One key conclusion is that learning remains a social process even in technology-enhanced learning contexts. So the technology-based proxies we construct need to retain and reflect the agency of the teacher. Technology-Enhanced Learning in Higher Education showcases some of the latest pedagogical technologies and their most creative, state-of-the-art applications to learning in higher education from around the world. Each of the chapters explores technology-enhanced learning in higher education in terms of either policy or practice. They contain detailed descriptions of approaches taken in very different curriculum areas, and demonstrate clearly that technology may and can enhance learning only if it is designed with the learning process of students at its core. So the use of technology in education is more linked to pedagogy than it is to bits and bytes.

Fluctuation-driven traffic congestion in a scale-free model of the Internet

In studies of complex heterogeneous networks, particularly of the Internet, significant attention was paid to analysing network failures caused by hardware faults or overload. There network reaction was modelled as rerouting of traffic away from failed or congested elements. Here we model network reaction to congestion on much shorter time scales when the input traffic rate through congested routes is reduced. As an example we consider the Internet where local mismatch between demand and capacity results in traffic losses. We describe the onset of congestion as a phase transition characterised by strong, albeit relatively short-lived, fluctuations of losses caused by noise in input traffic and exacerbated by the heterogeneous nature of the network manifested in a power-law load distribution. The fluctuations may result in the network strongly overreacting to the first signs of congestion by significantly reducing input traffic along the communication paths where congestion is utterly negligible. © 2013 IEEE.