In this module you will learn about a variety of Special Educational Needs and Disabilities (SENDs). You will look at the impact of various SENDs on a child's education and progress. The module focusses mainly on the following aspects of Special Educational Needs: - Developing a general understanding of various SENDs. - Different Educational settings - including Mainstream and Special Schools - Policy and provision for students with SEND - The models of disability - The views of Teachers, Students and other Professionals who work with children with SENDs. - Diversity and human rights - International perspectives on students with SEND and their inclusion
The Special Project module will allow you to produce a written assignment or equivalent on a topic of your choice, undertaking independent research with individual guidance and supervision sessions with your tutor. Meetings will be focused on readings selected in consultation with the convenor. In consultation with your supervisor, you will be encouraged to identify resources from the undergraduate curriculum which will help you to build your knowledge and understanding. This may be an entire module, individual lectures (recorded or live), or guidance provided on Blackboard from a single module or from a range of modules.
The Special Project module will allow you to produce a written assignment or equivalent on a topic of your choice, undertaking independent research with individual guidance from the convenor. The number of supervisions you have will depend on the size of the group you are in. If you are supervised alone or in a group of 2-3 you will normally have up to three supervisions of up to two hours over the course of the semester. Larger groups will have further supervisions, up to a maximum of eight meetings of up to two hours for very large groups. Meetings will be focused on readings selected in consultation with the convenor. In consultation with your supervisor, you will be encouraged to identify resources from the undergraduate curriculum which will help you to build your knowledge and understanding. This may be an entire module, individual lectures (recorded or live), or guidance provided on Blackboard from a single module or from a range of modules.
The Special Project (Text, Context, Intertext) module will allow you to write a written assignment or equivalent on a topic of your choice, undertaking independent research with individual guidance from the convenor. You will normally have three one-to-one supervisions of up to two hours over the course of the module. If two students elect to study the same topic, they may be offered four small seminars over the course of the module. Meetings will be focused on readings selected in consultation with the convenor. In addition, you will be encouraged to attend undergraduate lectures of relevance to the Special Project.
The Special Project (Text, Culture, Theory) module will allow you to produce a written assignment or equivalent on a topic of your choice, undertaking independent research with individual guidance from the convenor. The number of supervisions you have will depend on the size of the group you are in. If you are supervised alone or in a group of 2-3 you will normally have up to three supervisions of up to two hours over the course of the semester. Larger groups will have further supervisions, up to a maximum of eight meetings of up to two hours for very large groups. Meetings will be focused on readings selected in consultation with the convenor. In consultation with your supervisor, you will be encouraged to identify resources from the undergraduate curriculum which will help you to build your knowledge and understanding. This may be an entire module, individual lectures (recorded or live), or guidance provided on Blackboard from a single module or from a range of modules.
This module focuses on the dermatology, neurosciences, ophthalmology and head & neck knowledge and understanding, practitioner and professional skills required of an F1 doctor, and the assessments within this module will focus on these areas. The BM programmes are however highly contextualised and integrated programmes in which the application of knowledge and understanding, clinical skills and professional practice applicable to medicine are learned through a range of modules none of which are stand alone modules and therefore this module should be recognised by teachers and students alike as part of the whole year and programme. The Specialties Module in year 4 of the BM programmes is studied along with 4 other clinical teaching modules in Psychiatry, Acute Care, Obstetrics & Gynaecology/GUM and Child Health; a year long Medical Ethics & Law (MEL) module; and Year 4 and Finals assessment modules. The emphasis of the assessments for each of the modules aligns with the focus of learning for that module; however the integrated nature of the course means that there will undoubtedly be overlap and aspects of the assessment in each module will draw upon learning from modules studied in earlier years as well as modules studied in that year. In addition, the MEL module and Year 4 assessment modules have been purposely designed to assess learning outcomes covered in any of the 5 clinical modules from the year. The module will normally take the format of a 6 week placement in one or more of our University of Southampton partner trusts and primary care. The timing will vary for different student groups and the teaching staff will vary for different trusts and student groups. As is the nature of clinical placements, the exact learning experiences of each student will be variable; however all students will receive the same broad opportunities sufficient to achieve the learning outcomes of the module and it is expected that students will take responsibility for making the most of the opportunities provided and being pro-active in securing experiences in areas in which they feel they have weaknesses and/or have had fewest learning experiences.
How do writers activate and amplify the sonic properties of language? Why do artists use vocal performance of text in video art? How can text ‘perform’ on the page (or onscreen), and what does it mean for language to be performative? What does writing for performance require? How can audiences ‘read’ what is spoken, and how do they hear what is written? In this module, you will read a range of literary and critical material which explores the relation between writing, speaking and sounding. You will consider how contemporary artists deploy voiced text to incite political change and learn how 20th-century manifestos and 21st-century spells transform words into actions. You will analyse poetic texts as sonic artefacts and have the opportunity to write and perform innovative texts of your own. Over the course of the module, you will investigate what happens when language is lifted off the page.
This module begins with an assessment of the legacy of Lenin and goes on to investigate Stalin's rise to power and his methods in modernising economy and society. We will engage with historical debates concerning his role in the purges of the 1930s and the impact of the Great Patriotic War on his rule. We go on to evaluate the first effort at reforming the Stalinist system under his successor Khrushchev, whether there was a (partial) return to Stalinism under Brezhnev, and why the attempts to reform eventually failed under Gorbachev. We conclude by addressing Stalin's continuing popularity as a historical figure in post-communist Russia.
This is a first module on mechanics of solids and dynamics, which lays down the foundations of all of the aerospace structural modules that follow in subsequent years and also provides an introduction to dynamics that would be pertinent to structural vibration, control and aeroelasticity. - An introduction to mechanics of solids in one-dimension, as applicable to aerospace engineering - Preliminary extension solid mechanics to two and three dimensions. -Introduction to dynamics of rigid bodies and small oscillations.
This module consists of lecturers and associated practical sessions. The first part will focus on basic statistical programming in R. The second part will provide an introduction to some modern computational statistical methods and their implementation in R. The module includes 18 lectures and 18 computer practical sessions for students to gain hands-on experience of statistical programming and computation.
This module will provide an introduction to basic statistical programming in R. It consists of lecturers and associated practical sessions for students to gain hands-on experience of statistical programming.
The module provides an introduction to statistical programming in R. The module consists of lectures and computer labs for students to gain hands-on experience of statistical programming.
The module covers methods of disclosure control for tabular data and microdata, and how the utility of the resulting data is traded off against the risk of disclosure.
Functions of one and several random variables are considered such as sums, differences, products and ratios. The central limit theorem is proved and the probability density functions are derived of those sampling distributions linked to the normal distribution. Bivariate and multivariate distributions are considered, and distributions of maximum and minimum observations are derived. This module is a pre-requisite for all subsequent statistics modules, and desirable for Actuarial Mathematics I and II and Simulation and Queues
Statistical genetics has played a pivotal role in the discovery of genes that cause disease in humans. This module introduces the basic concepts and terms in genetics and demonstrates the use of statistical models to identify disease genes in humans.
This module develops some mathematical foundations of statistical inference: the theory of learning from data under uncertainty. We begin by studying a selection of useful tools and techniques from probability theory, including moment generating functions and transformations of random variables. Then we proceed to explore fundamental methods for point estimation, interval estimation and hypothesis testing, with a particular emphasis on maximum likelihood theory. We also introduce the framework of Bayesian inference and discuss the frequentist and subjective interpretations of probability.
Statistical inference involves using data from a sample to draw conclusions about a wider population. Given a partly specified statistical model, in which at least one parameter is unknown, and some observations for which the model is valid, it is possible to draw inferences about the unknown parameters and hence about the population from which the sample is drawn. As such, inference underpins all aspects of statistics. However, inference can take different forms. It may be adequate to provide a point estimate of a parameter, i.e. a single number. More usually, an interval is required, giving a measure of precision. It may also be necessary to test a pre-specified hypothesis about the parameter(s). These forms of inference can all be considered as special cases of the use of a decision function. There are a number of different philosophies about how these inferences should be drawn, ranging from that which says the sample contains all the information available about a parameter (likelihood), through that which says account should be taken of what would happen in repeated sampling (frequentist), to that which allows the sample to modify prior beliefs about a parameter’s value (Bayesian). This Module aims to explore these approaches to parametric statistical inference, particularly through application of the methods to numerous examples.
This module develops some mathematical foundations of statistical inference: the theory of learning from data under uncertainty. We begin by studying a selection of useful tools and techniques from probability theory, including moment generating functions and transformations of random variables. Then we proceed to explore fundamental methods for point estimation, interval estimation and hypothesis testing, with particular emphasis on maximum likelihood theory. We also introduce the framework of Bayesian inference and discuss the frequentist and subjective interpretations of probability.
Statistical learning and data science provide us with new forms of data and powerful new analysis tools. Advances in AI have allowed huge improvements in our ability to predict, but at the same time, these methods and data sources generate important ethical issues that we must consider. This module will provide students with the tools to appreciate the power of AI but also its limitations, and to be able to understand which AI tools might be suitable for particular tasks.
Statistical learning and data science provide us with new forms of data and powerful new analysis tools. Advances in AI have allowed huge improvements in our ability to predict, but at the same time, these methods and data sources generate important ethical issues that we must consider. This module will build upon previous modules (including ‘How AI works’) and provide students with the tools to appreciate the power of AI but also its limitations, and to be able to understand which AI tools might be suitable for particular tasks.
Statistical mechanics links the microscopic properties of physical systems to their macroscopic properties. Thermodynamics, which describes macroscopic properties, can then be derived from statistical mechanics with a few well motivated postulates. It leads to a microscopic interpretation of thermodynamic concepts, such as thermal equilibrium, temperature and entropy. In the course the basic principles of statistical mechanics will be introduced with applications to the physics of matter.
Statistical Methods for Finance is a critical module for you to learn basics for future modules on Econometrics, as well as their final year dissertation. This module covers important topics such as probability, discrete and random variables, Probability distributions, normal distribution, hypothesis testing, graphical analysis, correlation and simple regression. Lectures are followed by in-depth practical examples using tools that show the real world implications.
The main aim of the module is to provide the students with necessary knowledge of statistics and stochastic processes to carry out simple statistical procedures and to be able to develop simulation and other models widely employed in OR. The model is split into two parts: Statistics and Stochastic Processes.
