Conceptualisation and teaching of academic writing in an ESL context : a case study with first year university students.

The present thesis is based on a Case Study of an academic writing course, the Written English I module, in the Rwandan context of English as Second Language. The study was motivated by reports about low academic writing abilities of Rwandan students, abilities which were likely to be worsened by the recent decision of switching to English as the medium of instruction for almost all levels of the Rwandan education system. The study was also particularly motivated by the existing paucity of research on L2 writing in the African context. The study thus set out to investigate institutional discourses, conceptualisations, and teaching practices related to the module with focus on (a) the existing institutional discourse regarding academic literacy practices, (b) lecturers’ understanding of academic literacy and expectations of students, and (c) the pedagogical and assessment practices used in the module. The research was framed by pedagogic and academic literacy theories to understand conceptualisations and practices associated with the module under investigation. Indeed, the investigation was carried out with reference to recent developments in academic writing theories problematising the effectiveness of teaching approaches based on a series of decontextualised skills that students have to learn. The study was particularly underpinned by the view suggesting understanding students’ difficulties based on their language background and the specificity of the sociolinguistic context in which they have evolved. That is, the social practices model of academic writing which has the advantage of being inherently hybrid incorporating both the technical skills and the discourse practices demanded in the social institution of HE. The case study method was deemed appropriate as the focus was on understanding of the phenomenon in its natural setting with a particular attention to contextual conditions and experience of the participants (see Lacono, Brown, & Holtham, 2009). In terms of data collection methods, the study is based on a qualitative investigation using (i) analysis of documents such as handouts on ‘essay’ writing, assignment guidelines, samples of students’ writing, (ii) observation of classes, and (iii) interview of lecturers and a sample of students. With regard to findings, the research highlights challenges related to the dominance of an autonomous model – based on discrete skills – in teaching of academic writing in an ESL context, as is the case for the module investigated. Findings unveil a network of issues at institutional, conceptual and pedagogical levels. Analysis of the investigated teaching and learning process suggests the existence of a link between lecturers’ conceptualisations of academic writing and the form of practices used for development of competences in this activity. In other words, the practices as described are at a certain extent underlain by lecturers’ views of language and students, as well as what is involved in writing and learning to write. Further, through reflections on the practices in place for the Written English I module, findings of the study suggest paying attention to the approach consisting in viewing literacy as a social practice and problematisation of the autonomous model dominant in the investigated setting. Such an approach, used complementarily with technical skills addressing basic linguistic and structural skills of student-writers, is likely to facilitate novice-writers to find their own strategies to cope and adapt to the new practices and, on long-term, to constitute a new identity as members of academia. That is why, as indicated in the concluding part of the thesis, an argument is made in favour of a hybrid approach to teaching academic writing in L2 context. Such an approach is presented as likely to help in addressing students’ difficulties in terms of the linguistic rules as required by the writing conventions in use, but also in terms of meaning making in the complex disciplinary areas of HE. Concerning the form of provision of academic writing course geared to first year university students such as the Written English I module, it is to be regarded as a positive move that the provision investigated is located in the main curriculum of the institution as a subject rather than a form of writing support. This leads to the course being treated as a normal-status subject and mainstreamed in the academic activities to such a point that students take the course seriously. However, the course is not supposed to overlook particular requirements related to the context of learning or disciplinary areas of writing. Recommendations have also been made for further research. These are related to the need of an exploration at the NUR and other Rwandan HE institutions to find out what the general patterns characterising teaching of academic writing in the Rwandan HE are. Research has also been suggested into the assessment and feedback practices fore-grounded in the Rwandan HE and their impact on students’ engagement with the academic writing as well as the potential development of competences in that activity. Proposition of a study has also been made to examine how the critical issue of disciplinary writing is perceived and approached by lecturers in the Rwandan HE. This research, exploring lecturers’ perspectives on hybrid discourses and disciplinary genres in the academic writing course, is seen as likely to help in better illuminating the issue of monolithic conceptions of academic discourse which often characterise classroom practices

Deep Neural Networks and Tabular Data: Inference, Generation, and Explainability

Over the last decade, deep neural networks have enabled remarkable technological advancements, potentially transforming a wide range of aspects of our lives in the future. It is becoming increasingly common for deep-learning models to be used in a variety of situations in the modern life, ranging from search and recommendations to financial and healthcare solutions, and the number of applications utilizing deep neural networks is still on the rise. However, a lot of recent research efforts in deep learning have focused primarily on neural networks and domains in which they excel. This includes computer vision, audio processing, and natural language processing. It is a general tendency for data in these areas to be homogeneous, whereas heterogeneous tabular datasets have received relatively scant attention despite the fact that they are extremely prevalent. In fact, more than half of the datasets on the Google dataset platform are structured and can be represented in a tabular form. The first aim of this study is to provide a thoughtful and comprehensive analysis of deep neural networks' application to modeling and generating tabular data. Apart from that, an open-source performance benchmark on tabular data is presented, where we thoroughly compare over twenty machine and deep learning models on heterogeneous tabular datasets. The second contribution relates to synthetic tabular data generation. Inspired by their success in other homogeneous data modalities, deep generative models such as variational autoencoders and generative adversarial networks are also commonly applied for tabular data generation. However, the use of Transformer-based large language models (which are also generative) for tabular data generation have been received scant research attention. Our contribution to this literature consists of the development of a novel method for generating tabular data based on this family of autoregressive generative models that, on multiple challenging benchmarks, outperformed the current state-of-the-art methods for tabular data generation. Another crucial aspect for a deep-learning data system is that it needs to be reliable and trustworthy to gain broader acceptance in practice, especially in life-critical fields. One of the possible ways to bring trust into a data-driven system is to use explainable machine-learning methods. In spite of this, the current explanation methods often fail to provide robust explanations due to their high sensitivity to the hyperparameter selection or even changes of the random seed. Furthermore, most of these methods are based on feature-wise importance, ignoring the crucial relationship between variables in a sample. The third aim of this work is to address both of these issues by offering more robust and stable explanations, as well as taking into account the relationships between variables using a graph structure. In summary, this thesis made a significant contribution that touched many areas related to deep neural networks and heterogeneous tabular data as well as the usage of explainable machine learning methods