Teaching
Click a course title to read its full description
University of Amsterdam — 2024/2025, 2025/2026
University of Amsterdam — 2022/2023, 2023/2024, 2024/2025
University of Amsterdam — 2022/2023, 2023/2024, 2024/2025, 2025/2026
University of Amsterdam — 2022/2023, 2025/2026
University of Amsterdam — 2022/2023
STRUCTURES: Applying Responsible AI to Reduce Inequality
University of Amsterdam — BSc Computational Social Science — 2022/2023, 2023/2024, 2024/2025

Students study political science theories to assess structural issues, building on knowledge from the Foundation, Building Blocks, and Connections courses. They further deepen their understanding of qualitative and quantitative research methods and acquire skills in machine learning and prediction.

A major part of the semester is organised around a group project (theme: social inequality and algorithmic decision making). Students work in small project groups to address a real-world problem identified by CSSci staff, using real-world data. In the course of this project, they:

  • Design a viable research question approachable through social science and computational methods.
  • Work with a given dataset to create robust predictions.
  • Assess that prediction from a critical perspective and implement techniques to reduce bias.
  • Create professional presentations of their project and its deliverables.
  • Present the intervention plan to the CSSci community.

Additionally, there are five individual assignments to reinforce key course aims:

  • Algorithms and Social Inequality Portfolio.
  • Future Scenario Planning.
  • Prototype Ethics Tool.
  • Coding Portfolio and Explainer Session.
  • Individual contribution to Group Project V.
CONNECTIONS: Linking Data for Better Interventions in Health or Mobility Systems
University of Amsterdam — BSc Computational Social Science — 2022/2023, 2023/2024, 2024/2025, 2025/2026

Students study theories of social practices and systems thinking, building on knowledge from the Foundations and Building Blocks courses. They further deepen their understanding of qualitative and quantitative research methods and acquire skills in web scraping and machine learning.

A major part of the semester is organised around a group project (theme: health or mobility). Students work in small project groups to address a real-world problem identified by a CSSci Challenge Partner. In the course of this project, they:

  • Design a viable research question approachable through qualitative and quantitative research methods.
  • Compile a dataset from different sources — e.g. Challenge Partner's data, web scraping, statistical records, and other open-data sources.
  • Apply ethnographic, machine learning, and natural language processing techniques to answer the group project research question.
  • Create professional presentations of the project and its deliverables.
  • Present the intervention plan to the Challenge Partner and the CSSci community.

Additionally, there are five individual assignments to reinforce key course aims:

  1. Essay on Social Practices.
  2. Visualization of a (Sub)System.
  3. Ethnographic Observation Report.
  4. Coding Portfolio and Explainer Session.
  5. Individual Contribution to Group Project IV.
BUILDING BLOCKS: Experimenting with Digital Interventions for Behavioural Change
University of Amsterdam — BSc Computational Social Science — 2022/2023, 2025/2026

Students study theories of social and behavioural change, building on knowledge from the Foundation course. They further extend their theoretical base with theories of planned behaviour and human-computer interaction.

A major part of the semester is organised around a group project, in which they work in small groups to address a real-world problem identified by a CSSci Challenge Partner. In the course of this project, they:

  • Design a viable research question, including testable hypotheses.
  • Design and conduct a focus group and derive relevant insights.
  • Design an experiment, and conduct and reflect on a mini-trial.
  • Design a website applying UX design principles, through an iterative process informed by their research.
  • Create the website using relevant web development tools.
  • Create professional presentations of the project and its deliverables.

Additionally, there are five individual assignments to reinforce key course aims:

  1. Individual Contribution to Group Project III.
  2. Academic Literature Review.
  3. Inner Development for Societal Impact.
  4. Statistics Portfolio.
  5. Build an Informative Website.
CAPSTONE: Making Social Change with Digital Innovations
University of Amsterdam — BSc Computational Social Science — 2024/2025, 2025/2026

The Capstone project requires students to apply and integrate their social science and humanities expertise (SSHE), digital expertise (DE), research expertise (RE), and change-making expertise (CME) into a transdisciplinary project for an external partner. It serves as the final assessment where they demonstrate mastery of the intended learning outcomes of the CSSci programme. A significant portion of the semester is dedicated to independent group work, where they tackle problems presented by Project partners, with oversight from a Core lecturer.

Students are part of a project team tasked with developing, implementing, and delivering a Capstone project. They initiate, develop, and evaluate their project together with external partners such as governments, companies, and civil society organisations. Each project meets general criteria defined by the programme in terms of appropriate themes, methodological approaches, and potential empirical and theoretical entries. A crucial characteristic is that it challenges them to reach an interdisciplinary synthesis in consultation with the Project partner.

The Capstone runs the entire sixth semester and unfolds in three phases.

In the first phase, students determine — in close interaction with their partner organisation — the goals, priorities, and expected outputs of the project. They explore the societal challenge, identify stakeholders and their views, define decisive dilemmas, identify opportunities for change, and write a clear project plan. This phase concludes with a project proposal outlining all key steps.

In the second phase, they elaborate and refine their analysis of the societal challenge and develop a digital intervention, drawing on expertise from Project partners, Domain experts, and Core Lecturers. They design, test, evaluate, and refine a specific digital intervention while also developing an implementation plan. This phase concludes with the delivery of a (prototype of a) digital intervention and a final research report.

In the third phase, students write an individual Reflection essay outlining their contributions to the group project, critically discussing key steps taken, and evaluating processes and outcomes. The essay serves as the basis for an Explainer session with a Core lecturer, during which they defend substantive choices made by their team and reflect on their role and contributions throughout the programme.

To successfully complete their Capstone projects, student groups will:

  • Critically assess the landscape of existing solutions, identify gaps and opportunities for innovation, and develop interventions that effectively address social challenges.
  • Design and develop interventions grounded in robust theoretical frameworks.
  • Conduct research using a variety of social science methodologies and approaches.
  • Conduct research using a variety of data science methodologies and approaches.
  • Apply change-making skills to understand stakeholder perspectives and approach innovation projects with a prototyping mindset.
FOUNDATION: Appreciating the Complexity of Social Challenges
University of Amsterdam — BSc Computational Social Science — 2022/2023

Students become comfortable with the idea that different stakeholders experience and conceptualise social challenges in diverging ways. They explore, empathise with, understand, and even embrace these divergent perspectives before looking for common denominators, aligned interests, and opportunities for cooperation, intervention, and closure. To do so, they learn and practically exercise to:

  • Develop a theoretical and empirical understanding of the complexity of societal challenges and appreciate that these are open to multiple interpretations.
  • Apply the basics of data science to empirical research.
  • Conduct surveys and interviews as methods.
  • Apply structured project and product development and work efficiently and effectively in project teams.

A major part of the semester is organised around two projects (themes: climate change and surveillance). Students are introduced to design thinking and learn how to search, collect, close read, summarise, and reference scientific literature. They also take workshops on peer collaboration, time management, feedback, and reflection.

Additionally, they study theories that help them conceptualise the complexity of social challenges and the tension between structure and agency. They acquire a basic ability to identify stakeholders and explore their perspectives and interests. They gain knowledge and insight into various digital interventions at different levels of analysis and learn how these interventions may both alleviate and aggravate the problems at hand. Finally, they acquire basic insights into the governance of digitisation and underlying normative questions.

Students are introduced to the empirical cycle and practice with interviews, surveys, and related techniques for data analysis. The basics of computational thinking are also introduced, and they start to develop programming skills in Python. They work on their own survey data to develop skills in data wrangling and data visualisation. Lastly, they reflect on their group project and deliverables, their individual contribution, and their learning objectives.