5,892 research outputs found

    Identity, Power, and Prestige in Switzerland's Multilingual Education

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    Switzerland is known for its multilingualism, yet not all languages are represented equally in society. The situation is exacerbated by the influx of heritage languages and English through migration and globalization processes which challenge the traditional education system. This study is the first to investigate how schools in Grisons, Fribourg, and Zurich negotiate neoliberal forces leading to a growing necessity of English, a romanticized view on national languages, and the social justice perspective of institutionalizing heritage languages. It uncovers power and legitimacy issues and showcases students' and teachers' complex identities to advocate equitable multilingual education

    Qluster: An easy-to-implement generic workflow for robust clustering of health data

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    The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors

    A Consideration of Cooperative Learning to Enhance Pre-service Teachers’ Achievement in Tertiary English as a Foreign Language (EFL) Classrooms in Thailand

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    Cooperative learning has become a popular instructional practice around the world. It requires students working together in small groups to help support each other in maximising their own learning as well as that of others to accomplish a shared goal. A cooperative learning method, especially, Student Teams Achievement Divisions (STAD) developed by Slavin (1982) was implemented in the study. The study investigated the effectiveness of cooperative learning to enhance the English achievement of EFL (English as a foreign language) students in tertiary teacher education in Thailand. It also examined participants’ attitudes towards cooperative learning. The study began with a structured review of existing empirical studies to establish whether STAD could be a promising method to use in developing English proficiency in EFL and ESL (English as a second language) contexts. The review also helped identify the challenges and barriers to implementing the method and informed the primary research in terms of achievement tests, instructor training, time allowance for team study and material preparation. The review and synthesis of 28 studies revealed several beneficial suggestions regarding cooperative learning implementation in normal educational settings. However, the credibility of the overall evidence was weak, with most studies involving key methodological flaws. To examine the effectiveness of the method, a cluster randomised controlled trial (RCT) at the university level was used. The participants were 13 instructors and 614 students from 13 universities (forming 13 clusters). A total of eight universities that agreed to participate in the intervention were randomly assigned to experimental and control groups with four universities in each group. Another five universities agreed to complete the pre-test and post-test and are described in this thesis as an additional comparison group. The participating instructors were 13 Thai university instructors of English language from 13 Rajabhat Universities in Thailand. Their students were first-year pre-service teachers who were majoring in English in the Faculty of Education. The trial was carried out in one term consisting of 16 class sessions. The research instruments consisted of two parallel standardised English achievement tests, two attitude questionnaires (teacher and student) and classroom observations with ad hoc interviews. The results showed that the use of cooperative learning in tertiary EFL classrooms in Thailand is feasible. In terms of attitudes, both instructors and students were generally positive towards cooperative learning and supported its activities. Students in the treatment group did slightly better (ES = +0.09) when compared to all comparator groups. However, when considering the randomised experimental and control groups, the control group improved their post-test score (+0.26) while the experimental group declined (-0.20). Overall, cooperative learning showed no clear benefit for students’ English language achievement. The process evaluation revealed the key factors that facilitated the implementation were teacher training and support, preparation and availability of teaching resources and materials, teachers’ positive attitudes and the duration of cooperative learning instruction. Some barriers were also found, including students’ negative attitudes, inappropriate classroom settings and facilities, and instructors’ workload. Unfortunately, since the study was carried out during the COVID-19 pandemic, none of the universities were able to complete the course of 16 classes as planned. The number of classes students could meet in their normal classroom conditions was approximately 8 to 12. Different modes of lesson delivery (face-to-face, online and hybrid) were also reported. A replication of the study is needed for a more accurate assessment of the STAD method. Both the structured review and the cluster RCT suggest no strong evidence that the cooperative learning method, namely STAD, led to improved pre-service teachers’ English language achievement in Thailand. However, this does not necessarily mean the method does not work. The lack of impact might be due to the challenges faced in the delivery of the intervention during the pandemic. This was compounded by the lack of complete randomisation used in the study. It is, therefore, difficult to draw more definite conclusions about the effectiveness of STAD. It might be wise to conduct further robust evaluations involving a large number of educational institutions before any considerable investment can be made to introduce this method in higher education institutions in Thailand. In the meantime, there may be other approaches with a more promising evidence base which may enhance students’ English language achievement

    The Role of the Metabolome in the Development of Gestational Diabetes Mellitus in High-Risk Minority Women: A Causal Investigation

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    Gestational Diabetes Mellitus (GDM) is the most common pregnancy complication worldwide. However, GDM prevalence is substantially lower in white Europeans (WEs) compared to other ethnicities, especially South Asians (SAs) who experience the highest risk. Globally, healthy diet promotion is the mainstay in GDM prevention, however current guidelines are predominantly based on evidence from WEs. Furthermore, metabolic factors responsible for the disparities in prevalence are unknown but may offer guidance for improved prevention and management. This project aimed to (i) assess the association between diet and GDM across ethnic groups, (ii) determine if distinct metabolic profiles characterise GDM in SAs and WEs, and (iii) evaluate the presence of ethnic-specific causal associations between metabolites and gestational dysglycemia. Aims (ii) and (iii) utilised data from the Born in Bradford cohort (mean gestational age 26.1 weeks). First, through a systematic review of observational and randomised studies, pre-pregnancy diet was found to associate with GDM in WEs, but not in Asians. Secondly, the multivariate analyses of metabolites identified 7 metabolites that were characteristic of GDM in both ethnicities, with an additional 6 characteristic in WEs only. Finally, through Mendelian Randomisation (MR) analyses, 14 metabolites associated with pregnancy dysglycemia in WEs and 11 in SAs. No metabolites were identified in both ethnicities. Cholesterols and fatty acids were the most commonly identified classes identified in WEs and SAs, respectively. This project demonstrated (i) inconsistencies in the association between diet and GDM across ethnicities (ii) distinct metabolic profiles that associate with GDM in WEs and SAs and offers and supports the need for ethnic-specific manage GDM management strategies. In high-risk SAs, fatty acids may be the most important predictors of GDM. Future work should evaluate the role of pre-pregnancy fatty acid intake in GDM development in SAs to aid in the development of culturally tailored dietary interventions

    Graphical scaffolding for the learning of data wrangling APIs

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    In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction

    Masculinities, vulnerability and negotiated identity: Understanding the reporting behaviours of men who experience violence or otherwise harmful behaviour, within a sex work context

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    Context The focus of sex work related discussions most commonly falls on female providers of sexual services, and male purchasers. As a result, the often victim-oriented policy response in England and Wales falls short of truly addressing the needs of men who are involved in the sale of sex, with there being limited support available for them and a systemic approach which does not fully recognise the potential for men to face harm within this context. Methods The aim of this study is to explore experiences of and reactions to violence, and otherwise harmful behaviours, faced by men in the context of their sex working, by understanding the lived realities of a sample of men who engage in this type of work. The study takes a phased approach which combines an initial informative quantitative survey, with three subsequent phases of semi-structured interviews with male sex workers, sex work-focused practitioners and police officers. The method is guided by feminist research principles which suggest that reality is situated within those with lived experience, and also by an element of co-creation which has grounded this study within the perspectives of male sex workers from its conception. Findings The findings of this research suggest that all of the men involved in the study had faced at least one of the violent or otherwise harmful behaviours outlined, though reporting of these behaviours was not at all common. Discussions with the male sex working participants, practitioners and the police highlighted the issues related to the structural influences of authority, such as the police, and the social environment, and the internalisation of these wider factors, which create barriers to reporting for groups such as male sex workers and others who face similar social marginalisation. Conclusions This study challenges existing gendered understandings of violence and otherwise harmful behaviour within a sex work context, by highlighting the harmful experiences of men. By exploring these experiences and the reporting behaviours of those involved, the study also proposes a new framework for understanding barriers to reporting, which suggests that these are formed through the influences of formal and informal measures of social control, and the internalisation of these outside influences by the individual. By better understanding the experiences of men, and the barriers to their reporting, this study attempts to nuance a gendered discussion. Within, I propose that in order to better support male sex workers, responses must begin by appreciating the heterogeneity of those involved in sex work and the influence of their individual circumstances and the social environment on their willingness to seek support

    Machine learning and large scale cancer omic data: decoding the biological mechanisms underpinning cancer

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    Many of the mechanisms underpinning cancer risk and tumorigenesis are still not fully understood. However, the next-generation sequencing revolution and the rapid advances in big data analytics allow us to study cells and complex phenotypes at unprecedented depth and breadth. While experimental and clinical data are still fundamental to validate findings and confirm hypotheses, computational biology is key for the analysis of system- and population-level data for detection of hidden patterns and the generation of testable hypotheses. In this work, I tackle two main questions regarding cancer risk and tumorigenesis that require novel computational methods for the analysis of system-level omic data. First, I focused on how frequent, low-penetrance inherited variants modulate cancer risk in the broader population. Genome-Wide Association Studies (GWAS) have shown that Single Nucleotide Polymorphisms (SNP) contribute to cancer risk with multiple subtle effects, but they are still failing to give further insight into their synergistic effects. I developed a novel hierarchical Bayesian regression model, BAGHERA, to estimate heritability at the gene-level from GWAS summary statistics. I then used BAGHERA to analyse data from 38 malignancies in the UK Biobank. I showed that genes with high heritable risk are involved in key processes associated with cancer and are often localised in genes that are somatically mutated drivers. Heritability, like many other omics analysis methods, study the effects of DNA variants on single genes in isolation. However, we know that most biological processes require the interplay of multiple genes and we often lack a broad perspective on them. For the second part of this thesis, I then worked on the integration of Protein-Protein Interaction (PPI) graphs and omics data, which bridges this gap and recapitulates these interactions at a system level. First, I developed a modular and scalable Python package, PyGNA, that enables robust statistical testing of genesets' topological properties. PyGNA complements the literature with a tool that can be routinely introduced in bioinformatics automated pipelines. With PyGNA I processed multiple genesets obtained from genomics and transcriptomics data. However, topological properties alone have proven to be insufficient to fully characterise complex phenotypes. Therefore, I focused on a model that allows to combine topological and functional data to detect multiple communities associated with a phenotype. Detecting cancer-specific submodules is still an open problem, but it has the potential to elucidate mechanisms detectable only by integrating multi-omics data. Building on the recent advances in Graph Neural Networks (GNN), I present a supervised geometric deep learning model that combines GNNs and Stochastic Block Models (SBM). The model is able to learn multiple graph-aware representations, as multiple joint SBMs, of the attributed network, accounting for nodes participating in multiple processes. The simultaneous estimation of structure and function provides an interpretable picture of how genes interact in specific conditions and it allows to detect novel putative pathways associated with cancer
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