This third-year module aims to familiarize students with current topics in cognitive psychology and neuroscience, particularly those that are actively researched at the Centre for Perception and Cognition in the School of Psychology. Through lectures and tutorials, students will explore theoretical and experimental approaches to a wide range of topics including perception, attention and cognitive control, memory, learning, decision-making, neuroimaging, reading and the applications of AI in Psychology. This module stands out for its emphasis on the latest advancements in cognitive psychology and its efforts to establish methodological connections across different domains. Lectures offer a concise overview of key concepts in perceptual and cognitive processing, underscoring their significance in the ongoing research conducted by the lecturers. This enables an in-depth examination of recent research projects, exposing students to state-of-the-art techniques such as eye tracking, neuroimaging, and artificial intelligence. Real examples from lecturers' work offer firsthand experience of methods and concepts applicable in academic or non-academic careers.
This module aims to familiarize students with current topics in cognitive psychology and neuroscience, particularly those that are actively researched at the Centre for Perception and Cognition in the School of Psychology. Through lectures and tutorials, students will explore theoretical and experimental approaches to a wide range of topics including perception, attention and cognitive control, memory, learning, decision-making, neuroimaging, reading and the applications of AI in Psychology. This module stands out for its emphasis on the latest advancements in cognitive psychology and its efforts to establish methodological connections across different domains. Lectures offer a concise overview of key concepts in perceptual and cognitive processing, underscoring their significance in the ongoing research conducted by the lecturers. This enables an in-depth examination of recent research projects, exposing students to state-of-the-art techniques such as eye tracking, neuroimaging, and artificial intelligence. Real examples from lecturers' work offer firsthand experience of methods and concepts applicable in academic or non-academic careers.
This module has been developed to connect cutting-edge research in the field of strategy and innovation to teaching in the classroom. Particularly, this module leverages state-of-the-art research from tutors in the Strategy, Innovation and Entrepreneurship department, with the latest findings from their colleagues, operating in a global research environment. This enables learning about the latest developments in the strategy and innovation management field.
You will study approaches to curriculum design and development and examine the conceptions and models that support these processes. You will come to appreciate the complexities and issues that surround curriculum design, including the influences of stakeholders in managing and assessing the curriculum, and the reality of transforming theory into practice. A variety of contexts will be used to support your understanding of curriculum design, including the English National Curriculum and the curricula of other countries.
This module requires the learner to consider their particular environment in the context of wider curriculum design and development. It will require the student examines to compare and contrast different curriculum models. Students will explore appropriate models of evaluating education and training at work. This module is taught at M level.
This module will clarify the links between: identifying characteristics of consumers that can be measured or understood; the methods to measure or understand those characteristics; and how such measurements and understanding support marketing decision-making. It will focus upon how ubiquitous data from Internet can be used to understand and gain insight into consumption patterns and customer behaviour.
Understanding what motivates, influences and drives buyers to buy; be they consumers or business purchasers, is a critical area of learning in marketing. This module explores a range of buyer behaviour concepts, models and frameworks, enabling you to critically consider how these apply to different organisations and contexts. Further, this module supports you to connect with learning across your programme, in areas such as customer insight and marketing data analysis, to consider how understanding buyer behaviour can contribute to the development of effective and relevant marketing actions.
This module is designed to introduce you to the human dimension of cybercrime and cyber security. It is not coming from a technical perspective, but instead a critical criminological approach is applied to the topic. This means that we will be questioning theory, policy and practice, and discuss the way that this area might develop in the future.
This module covers two aspects of a pivotal intersection: applying security to defend machine learning and leveraging machine learning to enhance security. The aims at a high level are to: - Investigate security issues around machine learning systems - Review a variety of defence mechanisms for machine learning systems - Explore the use of machine learning in cyber security
Film Noir is one of Hollywood’s perennial cult genres, yet it is notoriously difficult to define, as it essentially amounts to a retrospective invention by critics. This module will attempt an understanding of the term through reference to its cultural contexts, placing the main corpus of the genre’s classics within its original historical moment of the 1940s and 1950s, as well as exploring its later Neo-Noir and global incarnations
Few names in the sciences are as widely recognised as Charles Darwin. His 1859 publication On the Origin of Species laid out the modern theory of evolution and secured his place in the history books. Today, evolutionary concepts permeate society well beyond biology. Competition between businesses, for example, is often described in terms of adaptation and ‘survival of the fittest’ – though this phrase was not actually coined by Darwin in On the Origin of Species. Despite his fame, most people still know relatively little about Darwin’s life and work. For example, after the publication of Origin, Darwin went on to write at least eight more books on topics ranging from the science of emotions to the ecology of worms. Darwin’s life spanned almost the entire Victorian era, a period marked not only by the integration of science into British culture and the industrial economy, but also by the rise of mass print culture and new media, the abolition of the slave trade, and increasing challenges to religious authority. In addition, the development of Darwin’s theory was embedded within global debates about gender, environment, politics, race, and empire—discussions that both preceded and long outlasted his life. This module examines the making of evolutionary thought in the nineteenth-century through the lens of Charles Darwin and his theory of evolution via natural selection. The aim is not to provide a straightforward biography, but to use Darwin as a focal point for exploring broader social and cultural issues central to the development of scientific modernity. In addition, this module will examine how myths and memories surrounding Darwin have been constructed throughout history, and how these constructions continue to permeate contemporary society. Organised thematically, the module will cover topics including: evolutionary theories before Darwin; evolution and gender; evolution and religion; Alfred Russel Wallace, the forgotten co-founder of evolution via natural selection; Darwin, empire and evolution beyond Britain; and evolutionary thought in the twentieth and twenty-first centuries.
This module provides an overview of key approaches to the analysis of quantitative and qualitative data in education. Building on the knowledge you gained about educational research design and data collection, you will learn how to turn your data into research findings, how to ensure the quality of your analysis, and how to present and communicate your findings.
Working with data of various forms is a crucial skill for all engineers and scientists. This module introduces students to working with, analysing and processing various different forms of data. The module focusses on ensuring students have a thorough grasp of the appropriate use of statistical and graphical measures to make decisions on data, and the basic practical tools and techniques required to filter, refine and query data. At its heart, this module provides the grounding for students to be able to perform Exploratory Data Analysis (EDA).
This module is intended to provide you with a blend of theory and current practice in organisational decision making and data management. The module critically discusses the complexity of organisational decision making by identifying key concepts and relevant theories. The module examines the role of knowledge in the contemporary organisations and explore the ways it can be used for better data management. Existing models for analysing decision making processes are examined, as well as how information systems and analytical tools can be used to tap into (big) data and support and enhance decision making within different organisational contexts.
This module will provide you with knowledge and understanding of data analytics to support decision making in modern marketing. You will explore the role of data analytics in marketing strategy and develop an insight into how to apply analytical tools to solve marketing problems, achieve ethical and sustainability goals and foster innovation. You will have the opportunity to learn through real-world case studies and experiment with different types of analytics. You will learn the principles, methods, and uses of data analytics in achieving marketing and business goals.
Given the importance of data analytics, this module provides students with a systematic and comprehensive understanding of the fundamentals of applied statistical modelling. It shows how statistical analysis can be used to solve civil and environmental engineering problems, using real-world case studies whenever possible. Exploratory data analysis, hypothesis testing, and regression analysis are main topics covered in this module. The main focus will be on developing regression models. Students will gain hands-on experience in using statistical software.
GGES3006 Data Collection & Research Methods for Sustainability and Environmental Science, is a module which aims to equip students with the skills necessary to plan and undertake independent research as part of their studies and later in their chosen careers. Students are introduced to different research methods (quantitative, qualitative and mixed methods), with an initial focus on core quantitative research methods. They are then given the option to either continue learning quantitative research methods or to switch to receiving complementary training in qualitative methods. In the first part of the module, students receive instruction on the fundamentals of quantitative data analysis. They are provided with relevant examples in Sustainability and Environmental Science and are given an opportunity to practice with these and write a quantitative report which contributes to the assessment of the module. They are also introduced to R programming language, which will be used throughout the module for all quantitative analyses. The second part of the module focusing on further quantitative methods aims to introduce the students to statistical techniques relevant to broader applications. The alternative option focusing on qualitative methods aims to provide training on key concepts used in qualitative research. Students are also given an opportunity to apply the skills acquired in this part of the module to a project leading to a research report, which will also form part of the assessment of the module.
GGES 6018, Data Collection & Research Methods for Sustainability and Environmental Science, is a module which aims to equip students on the MSc Sustainability and MSc Environmental Science programmes with the skills necessary to plan and undertake independent research as part of their studies and later in their chosen careers. Students are introduced to different research methods (quantitative, qualitative and mixed methods), with an initial focus on core quantitative research methods. They are then given the option to either continue learning quantitative research methods or to switch to receiving complementary training in qualitative methods. In the first part of the module, students receive instruction on the fundamentals of quantitative data analysis. They are provided with relevant examples in Sustainability and Environmental Science and are given an opportunity to practice with these and write a quantitative report which contributes to the assessment of the module. They are also introduced to R programming language, which will be used throughout the module for all quantitative analyses. The second part of the module focusing on further quantitative methods aims to introduce the students to statistical techniques relevant to data science applications. The alternative option focusing on qualitative methods aims to provide training on key concepts used in qualitative research. Students are also given an opportunity to apply the skills acquired in this part of the module to a project leading to a research report, which will also form part of the assessment of the module.
This module will cover the purposes and use of different methods for data collection in education research. It will address the design and use of questionnaires, different types of interviews and classroom observations. At the end of the module, students will have developed their skill in designing data collection instruments in connection to each of the three methods under focus and their critical understanding of the affordances and limitations of different methods.
This module studies how data is generated, valued, and monetised within digital ecosystems, as well as the ethical, legal, and technical challenges surrounding data ownership, privacy, and regulation. For example, how can we manage a music dataset produced by artists and used to train a generative AI model? What are the technical solutions to support selling and profit distribution of the generated model? What are the ethical and legal implications for artists and other actors involved? The module covers the data value chain, from collection and storage to integration, analysis, distribution, and monetisation, and the data governance issues associated with it.
