Course overview
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At LIS, you study problems. Forget about studying a single discipline, major, or minor. But don't mistake it for being any less academic.
To conquer these intricate problems, you'll explore a diverse range of interdisciplinary perspectives from the arts, social sciences, humanities and sciences. You'll uncover methods that sharpen your qualitative and quantitative skills, equipping you with the right tools to tackle each challenge head-on.
Say goodbye to tedious, never-ending exams. At LIS, you'll tackle real-life, tangible problems and collaborate with actual organisations. This hands-on approach allows you to apply your newfound skills and knowledge, generating valuable and impactful work that prepares you for the professional world.
Curious to know more? Keep scrolling!
Coaching
Our approach to coaching is designed to support synthesis and encourage metacognition; it is an opportunity for you and other students to come together in small groups, with the support of an academic tutor, to reflect on the learnings from different modules, and develop ideas about how to apply those learnings to tackling the problem.
Coaching creates a supportive environment where you can share the challenges of rigorous interdisciplinary learning with other students and faculty, while learning to think peripherally and laterally. It is through coaching that you will learn how to become a true interdisciplinarian.
Coaching will take place weekly in students’ Problems groups, and will be led by an academic tutor who’ll support students as they learn to work collaboratively and start to think in an interdisciplinary way.
The curriculum
Throughout Year 1, you’ll gain key knowledge and skills from a diverse range of disciplines, with real-world problems acting as a framework for your thinking. You’ll build on this foundation in Years 2 and 3, becoming equipped with an increasingly sharp, interdisciplinary problem-solving toolkit.
The content of our modules is subject to change as we revise our modules each year depending on student feedback, developments in the field, and the complex problems of the modern world.
In this module, students practice applying an interdisciplinary approach to a complex problem – one where there is no agreed solution, and where people disagree even about the nature of the problem.Problems 1a focuses on problems of inequality, e.g. big gaps in wealth, income, housing, health or education.
Students study two different disciplinary perspectives e.g. Neuroscience (how our environment influences our brain development), Network Science (how people influence each other through social networks), Political Economy (how government policies and economic markets interact) and Linguistics (how the language shapes thought and behaviour).
Knowledge and skills:
- Epistemology (theory of knowledge)
- Problem-framing
- Pitching and public speaking
This module introduces qualitative methods – tools for investigating aspects of the world that can’t easily be measured with numbers. Here, students focus on the difficulty in quantifying the emotional impact that a piece of writing or interaction with another person has on us.
How does it feel when you read something, or when you meet someone in a social setting? How and why does your reading or your encounter with that person have the impact that it does? This module provides ways of addressing these questions.
Knowledge and skills:
- Thematic analysis
- Close reading
- Participant observation
Quantitative Methods 1a is a foundational module that sets the stage for LIS’ programme of quantitative (i.e., numbers-based) methods, introducing learners to numerical and quantitative thinking.
Students develop order of magnitude estimation techniques (making estimates and approximate comparisons) before introducing basic scientific literacy skills and formulating questions that can be tackled quantitatively. The second half of this course uses Excel and Python to visualise data and to develop statistical methods.
Knowledge and skills:
- Coding (Python)
- Experimental methods
- Fermi estimation
This module builds on Problems 1a, introducing more concepts and skills that students can use to tackle complex problems. The problem area is global environmental change. Students work in groups to research a specific environmental and/or climate-related problem in their local area, in collaboration with a company, non-profit, or public sector institution.
They interview their ‘client’ about the different people and organisations involved, and then choose two disciplinary perspectives from four (currently Environmental Studies or Materials Science, and Politics or Law) to explore their research questions and write a consultancy report.
Knowledge and skills:
- Drawing and understanding connections
- Working with external organisations
- Stakeholder mapping
In Qualitative Methods 1b: Images and Systems, students explore visual language as a process that has multiple dimensions. The module starts by mapping information through mind maps and feedback loops. Students then learn to read the visual world by discussing and analysing images, before learning how to create them through photography and videography.
This module gives students skills for exploring forms of thinking that bring visual phenomena to the fore. Students will finish the module knowing how visual entities can be taken as structures for recollection, mapping, analysis, and even intervention on the world.
Knowledge and skills:
- Information mapping
- Photography
- Videography
The title of the course has two meanings. The first is that students will learn think about data: to analyse, summarise, and plot data – to find patterns and draw conclusions. We ask students to think about what the data means, how it could deceive, and how to avoid drawing the wrong conclusions.
The second meaning is using data to help you think. Students learn how to use data to think through the deep ideas of probability and uncertainty in evidence. On this journey, students will get an introduction to new approaches in data analysis, (“data science”) and start to learn about data science techniques such as machine learning
Knowledge and skills:
- Data science
- Statistics
- Machine learning
Problems 1c is the culmination of the first year, building on concepts and methods learned in all modules so far. Students still study a complex problem, but this time they choose it. They then design and develop an individual project under the guidance of a member of the faculty.
The result is a written study plus a visual or video product that presents the results of the study to all interested people, inside and outside of LIS. Problems 1c helps students to take stock of the progress they have made in their first year, and develop personal skills through planning, research, independent learning, networking, and initiative.
Knowledge and skills:
- Project management
- Independent research
- Networking
In Problems 2a, you’ll learn to think critically about technological innovation and contested developments. To do so, you’ll learn concepts from Legal and Political Theory, Data Studies, Technology and Culture Studies, and the Philosophy of AI.
This year's problem statement concerns the UK's approach to the regulation of artificial intelligence. Students will learn about “solutionism,” and will be encouraged to think critically about quick fixes to problems and evaluate the UK government's approach to the regulation of AI, using the concepts learnt in this module.
Knowledge and skills:
- Technology ethics
- Legal and Political theory
- Data studies
Problems 2b focuses on problems surrounding urban futures. What sort of future should we be enabling for our cities? How, why, and when? This module will move you from developing hypotheses to focusing on the design of interventions.
Through workshops and off-site urban exploratory walks and prototyping projects, you’ll be able to test and measure the impact of collaborative and interdisciplinary interventions to further the opportunities of urban futures.
Knowledge and skills:
- Architecture
- Prototyping
- Project management
Superconcepts are powerful ideas that originate in one discipline but go on to have far-reaching and creative applications in other disciplines. For example, evolution (from biology to memes in psychology) or entropy (from physics to migration flows in geography). Students learn the key points of some important superconcepts and apply these in creative ways to a real-world problem.
Mental Models are explanations of thought processes. They give insight into a variety of biases and heuristics that help us understand economics, business, politics and a range of social behaviour. Students have space to learn a range of mental models and to apply these to a problem of personal interest.
Knowledge areas:
- Superconcepts (big ideas that have changed the world)
- Mental models (understanding how and why people think as they do)
- Analogising and modelling
In Problems 2c, you'll bring together some of the Qual and Quant methods you have learnt in the second year and apply them to a complex real-world problem of your choosing. You'll start by reviewing what is known about the problem in different disciplines, and then bring together at least two different approaches in one ‘mixed methods’ study.
You'll also produce a short video or podcast excerpt to share your findings or outputs with a named professional audience of your choice. The video option allows for a lot of flexibility, from an animation or documentary, to showcasing a design, prototype, or series of artistic works.
Knowledge and skills:
- Independent study
- Mixed methods research
- Professional communication
Across the year, you will be able to select 3 methods from across the qualitative and quantitative methods options, of which one must be quantitative, and one must be qualitative. These electives will allow you to shape the direction of your learning by allowing you to build on existing skills or explore completely new methods.
What do you do when you write? You think on paper, digitally or otherwise. Writing up thoughts in the form of an argument results in better communication. Writing can be done in many ways, so different styles are often associated with different forms of thinking and doing. Thinking (writing) like a philosopher is not the same as thinking (writing) like a poet, or an architect.
In this module, we ask questions of interdisciplinarity by examining interdisciplinary thinking in the context of writing. We do this by focusing on one interdisciplinary kind of writing: manifesto writing. Some manifestos have the power to mix styles much in the same way as cross-disciplinary knowledge has the power to make us see things in new ways.
Knowledge and skill:
• Writing and communication
• Critical thinking
• Argumentation and storytelling
Starting with a recap of pre-calculus ideas, you will develop an understanding and fluency in the use of single-variable calculus techniques. This is the branch of mathematics that deals with the study of functions and their rates of change, which includes a mixture of analytical, numerical, and computer algebra tools.
You will learn to use differential equations to model real-world problems, e.g. disease spread, climate and energy. This includes learning the numerical techniques for solving differential equations and creating computer simulations. The end of the module is devoted to student-led projects culminating in a computational essay.
Knowledge and Skills:
• Dimensional analysis
• Calculus• Mathematical modelling
• Differential equations
• Computer simulations
In a world where everyone is competing for your attention, how can we tell the story of a complex issue like climate change? This module looks at the forms and structures which storytellers use — from a five-act play to a podcast, and on to a Virtual Reality platform.
Along the way, we consider the business models which support today’s media organisations. At the end of the modules, you’ll put it all together in an authentic multi-media campaign strategy which draws on everything you’ve learnt.
Knowledge and skills:
• Narrative and Storytelling
• Media techniques
• Business models
Network science is an important mathematical tool in today’s world, as many of our day-to-day environments are made of networks. In this module, you will learn to represent networks mathematically as ‘graphs’ and study how to find the shortest paths, cycles, tours and colourings. You will learn about equilibrium and dynamics in networks, and how to apply this to model epidemics.
Then, you will study matchings in graphs, which are related to mechanism design for allocation of indivisible resources. We will move on to game theory and how this is used to analyse strategies and choices in real-world situations in politics, economics and business. You will study strategic form games (e.g. prisoner’s dilemma, the commons problem), dominant strategies and Nash equilibria.
Knowledge and skills:
- Mathematical modelling
- Dynamics in networks
- Strategic thinking
- Mechanism design
This module provides you with methods used to collect, understand, interpret and create images. You’ll explore how images create meaning — images do not exist in isolation — by gathering visual information (recollection methods), creating an archive (analytic methods) and via your own creative research.
You’ll develop skills in camera (visual diaries), photogrammetry (the science of making reliable measurements through photographs), archival practices, collage, and creative research. This toolkit enables you to craft your own visual narratives: communication and interpreting the visual aspects of your wider world.
Knowledge and skills:
• Photography
• Curating and archiving
• Creative research methods
How does your phone predict the word you’re going to use next? How might we decode an alien signal if we received one? How can we figure out when documents share topics and when they don’t? This module teaches you how to analyse language at speed and scale using libraries in the Python programming language.
You’ll earn how to obtain large samples of language data and extract non-obvious insights from them. You’re also exposed to the basic principles of machine learning with language. At all points, you’re encouraged to use NLP in an interdisciplinary way to add to your learning in other modules.
Knowledge and skill:
• Natural Language Processing (using Python)
• Linguistics
• Data analytics
Design thinking is a human focused creative process to improve life, through design. It involves ‘thought and action’ to observe, speculate, respond, test, apply, evaluate and refine solutions to problems, to improve life for people and the planet. Its application is universal, ranging from products to services to processes at any scale and in any situation.
Key design thinking mindsets and models and methods have been adopted across professions and across the globe, proving particularly effective in addressing complex issues across and within social, economic, environmental and spatial conditions. Accessible by designers, non designers and end users alike, outcomes are wide ranging, enabling the creation of revolutionary innovation as well as incremental change through prototyping solutions where the financial inputs v. outputs are no less attractive than a traditional single input/output cost.
This module is an introduction to design thinking where we will look at different industries that practice it, why and when different methods are applied, what the impact is and what can be gained from these methods that can be applied across disciplines and problems.
Knowledge and skills:
• Design principles
• Empathy mapping
• Prototyping
• Ideation
This module uses the computer as a powerful tool to implement and explain the ideas we need for sound conclusions from noisy and complex data. You’ll add to your knowledge from your first year to build computational models that allow us to explain relationships with data, and to predict new data.
You’ll focus on “machine learning” approaches to data as they are increasingly dominant across academia, industry, law, and government. You’ll learn to understand these methods in order to understand and criticise their output and to build effective models of your own.
Knowledge and skills:
• Data science
• Modelling
• Machine learning
Material science is the science of stuff. Having a solid understanding of Material Science connects the world around you in a more holistic way. This module is for students who enjoy making and understanding what makes the world around them.
Whilst traditionally Material Science starts with studying the chemistry and physics of atoms, this module takes a ‘design-led’ approach. This means following a brief and its constraints, and from there, selecting and studying the properties of materials. The process involves both experimental making methods and rigour in following the material science data to inform the material selection process.
Key knowledge and skills:
- Experimentation and design
- Material science
- Sustainability
This module builds on other modules on the course, depending on the project in question and the knowledge and skills in quantitative and qualitative methods acquired up to the point of starting the capstone project. The primary mode of teaching is through supervision, and therefore this module also provides an experience of an extended supervision process.
Through this module, you will practice how to initiate and carry out an extended interdisciplinary research project and (where appropriate) how to undertake original research. You will consolidate your interdisciplinary research and problem-solving skills through the evidencing of sophisticated and correct research practice, academic conventions (such as sourcing, referencing etc) and communication. You will also consolidate a sophisticated understanding of the ethical issues that may underlie any extended research or other project.
In year 3, you’ll choose 5 options from a selection of modules, with at least 1x quantitative and 1x qualitative method included in your curriculum. You’ll also explore mixed methods in more depth. These final modules will help shape your Capstone Project.
The qualitative methods in Year 3 will directly support your research for your capstone project by providing advanced and immersive training in qualitative research strategies and techniques.
You’ll choose at least one and up to four qualitative and visual methods module.
Example modules:
- Design Thinking•
- Global Thinking
- Thinking Through Writing
- Applied Ethnography, Practical Ethics
- Communities & Campaigning
The quantitative methods in Year 3 will directly support your research for your capstone project by providing advanced and immersive training in quantitative research strategies and techniques.
You’ll choose at least one and up to four quantitative methods modules.
Example modules:
- Data Science and Machine Learning in Practice
- Intermediate Quantitative Modelling
- Advanced Case Studies in Data Science and Machine Learning
- Advanced Quantitative Modelling
- Simplifying Complexity
- Network and Games
Bringing together your work in different methods in both Year 1 and 2, this module extends your understanding of what it means to select and implement a range of methods for tackling a challenge. This module prepares you well for many master’s degree programme, as well as research in business, the public sector, and more.
To gain a degree in the UK you must pass a certain number of credits in each year of the degree. Each module is given a credit, which you are awarded when you pass each module at assessment.
We reserve the right to not run a module if there is insufficient student interest.
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