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BASc in Interdisciplinary Problems and Methods

BASc in Interdisciplinary Problems and Methods

Course overview

Problem-based learning (PBL) is at the core of the LIS philosophy to educate students to tackle complex, real-world problems.

You will start each term with a clear, important question relating to a complex challenge. Throughout the term, you will study various disciplines, and learn methods in multiple cycles, with each term ending in an assessment. In year 2, you will be able to select optional methods modules that will allow you to delve deeper into specific areas of interest. During year 3, you will also work on an individual Capstone Project.

Through building an interdisciplinary toolkit, you will develop cognitive flexibility; learning how to integrate new and existing knowledge, select the theories and concepts most appropriate to a challenge, and how to apply these to deliver real and tangible results.

The degree

Problem-based learning

During each term, you will be introduced to a specific problem through a range of perspectives, including stakeholders, businesses and organisations involved in tackling the problem today.  

You will be presented with a clear problem statement, which will guide your approach to tackling the challenge by producing tangible outputs. These statements will constantly evolve to reflect the times, allowing you to work on the challenges that are most pressing. 

Understanding the nature of complex challenges and how to tackle them is a foundational skill you will build early in their degree, and will be transferrable to your future work.

Watch this video about problems to find out more

Disciplinary Perspectives

In two-weekly cycles, you will study different disciplinary perspectives from across arts and science fields, which will serve as an exploration of foundational disciplinary concepts and critical pillars of knowledge as they apply to specific challenges. 

While you will learn from multiple disciplines simultaneously, you will also have the opportunity gain further knowledge in your areas of choice as you advance through your degree.

You will graduate with an understanding of theories and concepts from multiple fields of study, which will allow you to understand challenges through different disciplinary perspectives.

Watch this video about disciplinary perspectives to find out more


Methods are ways of doing things that will enable you to apply what you know. They are tools and techniques, literacies and competencies, designed to improve your learning by doing, and to help you take action in the world.

Throughout the degree, you will develop qualitative and quantitative skills. You will learn to play with words and numbers, to code and to narrate, to experiment and to interpret. You will learn how to identify the right tools for each challenge, and to combine your knowledge with tangible skills, beyond boundaries of discipline or field.

The research methods and skills you will learn at LIS are foundational and transferable. They will help you to navigate the challenges addressed throughout your degree and will prepare you to tackle challenges in the professional world, where problems do not respect subject silos and require inventive solutions.

Watch this video about methods to find out more


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.

Challenge Examples


Degree Structure

Year 1
Year 2
Year 3

During Year 1 of the BASc degree, you will develop foundational interdisciplinary skills such as learning to analyse challenges, developing cognitive flexibility, and exploring concepts from diverse range of academic disciplines.

Please note that the problems, methods, and disciplinary perspectives listed below are indicative and may be subject to change in future years.

In year 2, you’ll have the opportunity to tackle a broader range of complex challenges, such as Technology and Ethics or Urban Design, involving group work and independent work, as well as more advanced concepts from multiple disciplines. You will also begin to study mental models and superconcepts, extending your interdisciplinary knowledge. 

Please note that the problems, methods, and disciplinary perspectives listed below are indicative and may be subject to change in future years.

Year 3 is an opportunity for you to exhibit the sum of your learning in interdisciplinary problem-solving and methods, by working independently to tackle a problem you care about.

Please note that the problems, methods, and disciplinary perspectives listed below are indicative and may be subject to change in future years.

Problems 1a
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(18 credits) 

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 
Qualitative Methods 1a: Language and Communication
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(15 credits)

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: Thinking Like a Scientist
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(15 credits)

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 
Problems 1b
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(18 credits)

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 
Qualitative Methods 1b: Images and Systems
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(15 credits)

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 
Quantitative Methods 1b: Thinking Through Data
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(15 credits)

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: Independent Work
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(24 credits)

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 
Problems 2a
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(18 credits)

In Problems 2a, students learn to think critically about technological innovation and contested developments. To do so, students learn concepts from Legal and Political Theory, Data Studies, Technology and Culture Studies, and Supply Chain Management. 

Students have the opportunity to focus on a variety of related technology problems, for example the ethical implications of neural implants, the use of new technologies in times of crisis, the impact of social media bubbles, and whistle-blowers in the technology industry. 

Knowledge and skills: 

  • Technology ethics 
  • Legal and Political theory 
  • Supply chains 
Problems 2b
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(18 credits)

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 students from developing hypotheses to focus on the design of interventions. 

Through workshops and off-site urban exploratory walks and prototyping projects, students will be able to test and measure the impact of new collaborative and interdisciplinary interventions to further the opportunities of urban futures. 

Knowledge and skills: 

  • Architecture 
  • Prototyping 
  • Project management 
Mental Models and Superconcepts: Interdisciplinary Ways to View the World
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(15 credits)

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 
Problems 2c: Individual Project
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(24 credits)

In Problems 2c, students bring together some of the Qual and Quant methods they have learnt in the second year and apply them to a complex real-world problem of their own choice. They 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. 

They also produce a short video or podcast excerpt to share their findings or outputs with a named professional audience of their 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
Elective Methods Modules
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(15 credits)

During each termyou 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. 

Thinking Through Writing
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 different 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 for that matter.

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.
Social Science Research Methods
This module is a deep dive on how qualitative methods are used in research projects. Focussing on social problems, students discover how past researchers carried out their projects by looking at a set of classic and current case studies in social science.

From this, students gain a comprehensive understanding of the benefits and limitations of qualitative research. This helps students later on, when they gain hands on experience of managing their own qualitative study by collecting data via interviews, focus group discussion, and participant observation.
Negotiated Learning
This module allows students to take advantage of the vast array of information that is now available online. Students are supported to pursue their own learning and use the knowledge and skills that they gain to address a complex, real-world problem of their choice.

At the end of the module, students produce two reports of their work. One is aimed at a policy or professional audience - people who may be able to take specific steps to address the problem. The other is aimed at an academic audience, who may be interested in more general approaches to the problem that the student has investigated.
Intermediate Quantitative Analysis: Investigating the Physical World
Starting with a recap of pre-calculus ideas, students develop an understanding and fluency in the use of single variable calculus techniques (the branch of mathematics that deals with the study of functions and their rates of change), which include a mixture of analytical, numerical, and computer algebra tools.

Students model real-world problems in the form of differential equations. Example questions include modelling disease spread, climate, and energy. The end of the module is devoted to student-led projects culminating in a computational essay.
Telling the Story of a Wicked Problem
In the 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 module, students put it all together an authentic multi-media campaign strategy which draws on everything they have learnt.
Further Statistics and Probability
Real-world data is ‘messy’ with no obvious patterns. This module, therefore, builds on the foundations of first year quantitative methods at LIS to explore a range of more advanced statistical and probabilistic methods for real-world problems.

Students go deeper into the mathematical theory behind well-known techniques, particularly focussing on the ‘captain’ of the module – Bayes' Theorem. Quantitative confidence in applying these methods on big data is built through regular application of coding in Python.
Visual Methods: From Documentation to Transformation
This module provides students with methods used to collect, understand, interpret and create images. Students will explore how images create meaning – images do not exist in isolation – by gathering visual information (recollection methods), creating an archive (analytic methods) and via their own creative research.

Students develop skills in camera journalling (visual diaries), photogrammetry (the science of making reliable measurements through photographs), archival practices, collage, and creative research. This toolkit enables students to craft their own visual narratives: communicating and interpreting the visual aspects of their wider world.
Introduction to Natural Language Processing (NLP)
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 students how to analyse language at speed and scale using libraries in the Python programming language.

Students learn how to obtain large samples of language data and extract non-obvious insights from them. They are also exposed to the basic principles of machine learning with language. At all points, students are encouraged to use NLP in an interdisciplinary way to add to their learning on other modules.
Design Thinking
Design thinking is a problem-solving approach with the intention to improve products. But design thinking has not developed in insolation - it was born from best practices in experimental design from the sciences, creativity from the arts, prioritisation from business etc., all filtered through a human-centred lens.

Students will study the structures of common design processes as well as ideation techniques (coming up with ideas and concepts), before the implementation of their own project.
Data Science
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. Students add to their knowledge from their first year to build computational models that allow us to explain relationships with data, and to predict new data.

Students will focus on “machine-learning” approaches to data as they are increasingly dominant across academia, industry, law and government. Students learn to understand these methods in order to understand and criticise their output, and to build effective models of their own.
Materials and Making
The digital age removes contact with the physical environment. Lost in cyber space? Craving re-connection and re-education of all things material and grounded?

This module involved entails learning about the world in a tangible and concrete way, via materials and making. The module covers topics ranging from the history of our elements, traditional making methods, cultures of making, and food and agriculture as materials in the world.
Capstone Project
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30 credits

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. 

Elective Methods Modules
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(15 credits)

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.

Qualitative Methods (15 credits)
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 qualitative and visual methods module.
Example modules:
- Stories and Campaigns: Communications To Shape The World
- Social Exploration and Excavation – Qualitative Research in
- Apparatus, Process and Subjective Methodologies in
Photography and Videography
- Global Citizenship
- Skills for Sustainability
Quantitative Methods (15 credits)
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 quantitative methods module.
Example modules:
- Complexity and Modelling Systems
- Further Data Engineering
- Investigating The Physical World II: Dynamic Physical Systems
- Machine Learning and Artificial Intelligence
- Climate Change and Planetary Health
Mixed Methods
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(15 credits)

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.

Credits are accumulated towards the total credit required for the degree. To get a BASc (Honours), you need to get a total of 360 credits over three years. Our degree is structured so that you complete 120 credits in Year 1, up to 135 in Year 2, and at least 105 credits in Year 3.

Attend an Open Day

During these half-day taster events you will meet members of the faculty and wider LIS team as well as other potential applicants. You will get greater insight into the course content, we’ll explore a real-world problem such as plastic pollution or the impact of social media on mental health, and consider different ways to tackle it.

Visit our events page to register for an upcoming Discovery Day.

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