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Widening Participation in Higher Education: Exploring the Factors that Impact on Black Asian and Minority Ethnic students attainment at a Post-1992 University in England
No full textThe awarding gap between black, Asian and minority ethnic (BAME) and widening participation (WP) students in general, and white students has continued to be of interest amongst researchers in the quest for a coherent social justice-based policy and practice in the UK higher education (HE) system. Having greater access to HE has not meant greater attainment for BAME students. This study used an exploratory and qualitative approach to investigate the lived experiences of BAME students and staff with respect to what impacts on the awarding gap, focusing on the context of post-1992 universities and the academic, cultural and social aspects as units of analysis. This approach offers a departure from the mainly deficit approach which predominantly results in interventions that presuppose fixing or aligning the students to existing lowly prioritised structures of support. The evidence found support to an approach that prioritises strategic and deeper pedagogic reforms and support systems that recognise the cultural capital of the BAME students. The analysis also gave us the opportunity to conclude that if the stakes are high enough for higher education institutions, their strategic planning processes and operational systems would naturally pick up and develop effective social justice policies
El retrato de Margarita van Eyck de Jan van Eyck: nuevas imágenes del Proyecto VERONA (Van Eyck Research in Open Access)/ Jan van Eyck’s Portrait of Margaret van Eyck. New Imaging by the VERONA Project (Van Eyck Research in OpeN Access)
No full textThe application of a new tool of scientific analysis known as macro X-ray fluorescence (MA-XRF) scanning to the study of Jan van Eyck’s signed and dated Portrait of Margaret van Eyck (1439) demonstrates for the first time how Van Eyck changed the composition of the portrait at the painting stage. Along with X-radiography and infrared reflectography, among other technical methods, MA-XRF scanning reveals that an initial decision to alter the pose of the sitter’s hands initiated a series of other adjustments. It is now clear that the original pose mirrored that in a portrait of Isabella of Portugal, duchess of Burgundy, changing our understanding of one of the most significant portraits in Van Eyck’s oeuvre and shedding new light on Van Eyck’s process. The results offer a new basis for fresh research on topics connected to the portrait, such as identity, gender, social mobility and memory.
The painting was examined as part of the VERONA project (Van Eyck Research in OpeN Access), which created high-resolution technical images of the oeuvre of Van Eyck and his circle, adopting a single, standardised imaging protocol for all of the paintings. The new imaging includes macrophotography (normal light, raking light, infrared and ultraviolet fluorescence), infrared reflectography and in some cases radiography and MA-XRF scanning. The project had 29 museum partners across Europe and the US; it was funded by BELSPO (the Belgian Federal Science Policy), in Brussels. In 2018, the resulting images were made available for research in open access on a specially designed website: Closer to Van Eyck | Further Works by Jan van Eyck (kikirpa.be)
Machine learning based diabetes classification and prediction for healthcare applications
Get PDFThe remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications
Polarizations, exclusionary neonationalisms and the city
Get PDFPolitical geographers have recently renewed conversation on the spatialities of exclusionary neonationalism, surfacing in the form of right-wing political populism (Casaglia et al., 2020), Islamophobia (Antonsich, 2018; Koch & Vora, 2020) and neo-colonial relations (Avni, 2020). These insightful commentaries, however, are yet to address an important political-geographic dimension of the phenomenon: the growing schism between metropolitan and nationalist politics, which we conceptualize here as double polarization. The spatial and political consequences of this emergent dynamic, we contend, call for new articulations
of urban political geography
The dyslexic student’s experience of education
Get PDFThis article explores the lived experiences of students with dyslexia at public universities. The researcher conducted a qualitative phenomenological study using Interpretative Phenomenological Analysis and collected data via semi-structured, in-depth interviews from university students who have all been formally diagnosed with dyslexia. Three main themes emerged from the data: (1) barriers to learning (2) coping strategies employed by dyslexic students, and, (3) the support offered by university structures. The findings indicated that dyslexic students experience a significant amount of anxiety at university, and this is often linked to the lack of societal awareness of dyslexia by academic staff. The findings also revealed that although dyslexic students face barriers to learning that they must overcome, they are equipped with self-taught coping mechanisms. The main conclusion that can be drawn from this study is that if dyslexic students receive the necessary and appropriate support, they can be as successful as their non-dyslexic peer
Understanding vehicular routing behavior with location-based service data
Get PDFProperly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation
