Essex Adult Community Learning: PTLLS for distance and blended learning

Funded by the LSIS Excellence and Improvement Fund, Essex Adult Community Learning has produced a distance/blended learning resource on CD-Rom for tutors in the post-16 sector to achieve the City & Guilds PTLLS (7303 Preparing to Teach in the Lifelong Learning Sector) at Level 4. The aim of the resource is to improve teaching and learning by providing a flexible way to access initial teacher training where candidates may otherwise find it difficult or impossible to attend a taught course. It is also intended to increase tutors' own e-learning skills.

Case study: Technology - a fundamental aspect of teaching, learning and assessment

Case study on how Reading College is taking a holistic approach to developing their digital strategy, focusing on good practice in teaching and learning and extending learning beyond the classroom walls.

Learning from implementation of community selection in Zambia, Solomon Islands, and Bangladesh AAS hubs

The CGIAR Research Program on Aquatic Agricultural Systems (AAS) is a research in development program which aims to foster innovation to respond to community needs, and through networking and social learning to bring about development outcomes and impact at scale. It aims to reach the poorest and most vulnerable communities that are dependent upon aquatic agricultural systems. AAS uses monitoring and evaluation to track progress along identified impact pathways for accountability and learning. This report presents an evaluation of the recommended method for selecting communities during the participatory planning process, referred to as AAS “hub rollout,” in the first year of program implementation.

Ageing, Adaption and Accessibility: Time for the Inclusive Revolution!

Information and Communication Technology (ICT) is becoming increasingly central to many people’s lives, making it possible to be connected in any place at any time, be unceasingly and instantly informed, and benefit from greater economic and educational opportunities. With all the benefits afforded by these new-found capabilities, however, come potential drawbacks. A plethora of new PCs, laptops, tablets, smartphones, Bluetooth, the internet, Wi-Fi (the list goes on) expect us to know or be able to guess, what, where and when to connect, click, double-click, tap, flick, scroll, in order to realise these benefits, and to have the physical and cognitive capability to do all these things. One of the groups most affected by this increase in high-demand technology is older people. They do not understand and use technology in the same way that younger generations do, because they grew up in the simpler electro-mechanical era and embedded that particular model of the world in their minds. Any consequential difficulty in familiarising themselves with modern ICT and effectively applying it to their needs can also be exacerbated by age-related changes in vision, motor control and cognitive functioning. Such challenges lead to digital exclusion. Much has been written about this topic over the years, usually by academics from the area of inclusive product design. The issue is complex and it is fair to say that no one researcher has the whole picture. It is difficult to understand and adequately address the issue of digital exclusion among the older generation without looking across disciplines and at industry’s and government’s understanding, motivation and efforts toward resolving this important problem. To do otherwise is to risk misunderstanding the true impact that ICT has and could have on people’s lives across all generations. In this European year of Active Ageing and Solidarity between Generations and as the British government is moving forward with its Digital by Default initiative as part of a wider objective to make ICT accessible to as many people as possible by 2015, the Engineering Design Centre (EDC) at the University of Cambridge collaborated with BT to produce a book of thought pieces to address, and where appropriate redress, these important and long-standing issues. “Ageing, Adaption and Accessibility: Time for the Inclusive Revolution!” brings together opinions and insights from twenty one prominent thought leaders from government, industry and academia regarding the problems, opportunities and strategies for combating digital exclusion among senior citizens. The contributing experts were selected as individuals, rather than representatives of organisations, to provide the broadest possible range of perspectives. They are renowned in their respective fields and their opinions are formed not only from their own work, but also from the contributions of others in their area. Their views were elicited through conversations conducted by the editors of this book who then drafted the thought pieces to be edited and approved by the experts. We hope that this unique collection of thought pieces will give you a broader perspective on ageing, people’s adaption to the ever changing world of technology and insights into better ways of designing digital devices and services for the older population.

Statistics anxiety, trait anxiety, learning behavior, and academic performance

The present study investigated the relationship between statistics anxiety, individual characteristics (e.g., trait anxiety and learning strategies), and academic performance. Students enrolled in a statistics course in psychology (N=147) filled in a questionnaire on statistics anxiety, trait anxiety, interest in statistics, mathematical selfconcept, learning strategies, and procrastination. Additionally, their performance in the examination was recorded. The structural equation model showed that statistics anxiety held a crucial role as the strongest direct predictor of performance. Students with higher statistics anxiety achieved less in the examination and showed higher procrastination scores. Statistics anxiety was related indirectly to spending less effort and time on learning. Trait anxiety was related positively to statistics anxiety and, counterintuitively, to academic performance. This result can be explained by the heterogeneity of the measure of trait anxiety. The part of trait anxiety that is unrelated to the specific part of statistics anxiety correlated positively with performance.

Traversing digital learning landscapes: an Institutional and faculty case study.

Within the UK, there is a growing awareness to better understand what online educational technologies can offer in relation to learning and teaching, and how social technologies are changing communication and collaboration out with formal education. The concept of the ‘digital university’ is being widely debated within the UK Higher education sector (McCluskey and Winter, 2012), becoming embedded in educational policy, and beginning to be explored within many institutions. This session will report on one such institutional initiative, undertaken at Edinburgh Napier University in Scotland. A Digital Futures Working Group was established to: benchmark best practice in key areas including digitally enhanced education and digital literacies development; identify areas for short term action; and to produce a robust ‘digital agenda’ to inform the future direction of the university. Pivotal to this was the recognition to evolve staff digital pedagogical practices and to harness emerging digital opportunities, meet learner expectations, and meet wider expectations for contemporary able citizens. This session will be delivered in two parts. Firstly we will provide an insight into the focus of the project and the rich picture methodology used to consult with staff and students. Secondly we will specify the outcomes produced, and provide a case study of how the Faculty of Health, Life and Social Sciences engaged with the process and the progression of their digitally enabled educational practices.

Genes and the ageing muscle: a review on genetic association studies

Western populations are living longer. Ageing decline in muscle mass and strength (i.e. sarcopenia) is becoming a growing public health problem, as it contributes to the decreased capacity for independent living. It is thus important to determine those genetic factors that interact with ageing and thus modulate functional capacity and skeletal muscle phenotypes in older people. It would be also clinically relevant to identify 'unfavourable' genotypes associated with accelerated sarcopenia. In this review, we summarized published information on the potential associations between some genetic polymorphisms and muscle phenotypes in older people. A special emphasis was placed on those candidate polymorphisms that have been more extensively studied, i.e. angiotensin-converting enzyme (ACE) gene I/D, α-actinin-3 (ACTN3) R577X, and myostatin (MSTN) K153R, among others. Although previous heritability studies have indicated that there is an important genetic contribution to individual variability in muscle phenotypes among old people, published data on specific gene variants are controversial. The ACTN3 R577X polymorphism could influence muscle function in old women, yet there is controversy with regards to which allele (R or X) might play a 'favourable' role. Though more research is needed, up-to-date MSTN genotype is possibly the strongest candidate to explain variance among muscle phenotypes in the elderly. Future studies should take into account the association between muscle phenotypes in this population and complex gene-gene and gene-environment interactions.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

How Does the Cerebral Cortex Work? Developement, Learning, Attention, and 3D Vision by Laminar Circuits of Visual Cortex

A key goal of behavioral and cognitive neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how the visual cortex sees. Visual cortex, like many parts of perceptual and cognitive neocortex, is organized into six main layers of cells, as well as characteristic sub-lamina. Here it is proposed how these layered circuits help to realize the processes of developement, learning, perceptual grouping, attention, and 3D vision through a combination of bottom-up, horizontal, and top-down interactions. A key theme is that the mechanisms which enable developement and learning to occur in a stable way imply properties of adult behavior. These results thus begin to unify three fields: infant cortical developement, adult cortical neurophysiology and anatomy, and adult visual perception. The identified cortical mechanisms promise to generalize to explain how other perceptual and cognitive processes work.

Spikes, Synchrony, and Attentive Learning by Laminar Thalamocortical Circuits

This article develops the Synchronous Matching Adaptive Resonance Theory (SMART) neural model to explain how the brain may coordinate multiple levels of thalamocortical and corticocortical processing to rapidly learn, and stably remember, important information about a changing world. The model clarifies how bottom-up and top-down processes work together to realize this goal, notably how processes of learning, expectation, attention, resonance, and synchrony are coordinated. The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current-source densities and local field potentials; and single cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. In particular, the model predicts how laminar circuits of multiple cortical areas interact with primary and higher-order specific thalamic nuclei and nonspecific thalamic nuclei to carry out attentive visual learning and information processing. The model simulates how synchronization of neuronal spiking occurs within and across brain regions, and triggers STDP. Matches between bottom-up adaptively filtered input patterns and learned top-down expectations cause gamma oscillations that support attention, resonance, and learning. Mismatches inhibit learning while causing beta oscillations during reset and hypothesis testing operations that are initiated in the deeper cortical layers. The generality of learned recognition codes is controlled by a vigilance process mediated by acetylcholine.

Simulating Effects of Learning and Lesions with a Model of Intrinsic and Synaptically Gated Responses of Striatal Cholinergic Interneurons

The giant cholinergic interneurons of the striatum are tonically active neurons (TANs) that respond with characteristic pauses to novel events and to appetitive and aversive conditioned stimuli. Fluctuations in acetylcholine release by TANs modulate performance- and learning-related dynamics in the striatum. Whereas tonic activity emerges from intrinsic properties of these neurons, glutamatergic inputs from thalamic centromedian-parafascicular nuclei, and dopaminergic inputs from midbrain, are required for the generation of pause responses. No prior computational models encompass both intrinsic and synaptically-gated dynamics. We present a mathematical model that robustly accounts for behavior-related electrophysiological properties of TANs in terms of their intrinsic physiological properties and known afferents. In the model, balanced intrinsic hyperpolarizing and depolarizing currents engender tonic firing, and glutamatergic inputs from thalamus (and cortex) both directly excite and indirectly inhibit TANs. If the latter inhibition, presumably mediated by GABAergic interneurons, exceeds a threshold, its effect is amplified by a KIR current to generate a prolonged pause. In the model, the intrinsic mechanisms and external inputs are both modulated by learning-dependent dopamine (DA) signals and our simulations revealed that many learning-dependent behaviors of TANs are explicable without recourse to learning-dependent changes in synapses onto TANs. The "teaching signal" that modulates reinforcement learning at cortico-striatal synapses may be a sequence composed of an adaptively scaled DA burst, a brief ACh burst, and a scaled ACh pause. Such an interpretation is consistent with recent data on cholinergic control of LTD of cortical synapses onto striatal spiny projection neurons.