The aim of this thesis was to examine what teachers want to learn themselves. The main research question was: what, how and why teachers want to learn? And does this depend on their years of teaching experience and the school at which they work?

The Reinforcement Learning lab conducts research into Reinforcement Learning and Intelligent Combinatorial Algorithms.

Computers are capable of making incredibly accurate predictions on the basis of machine learning. In other words, these computers can learn without intervention once they have been pre-programmed by humans. At LIACS, we explore and push the borders of what a revolutionary new generation of algorithms…

The programme is also offered in a blended learning version: this is a combination of distance learning and face-to-face learning. Read more information

This thesis investigates the contribution of quantum computers to machine learning, a field called Quantum Machine Learning. Quantum Machine Learning promises innovative perspectives and methods for solving complex problems in machine learning, leveraging the unique capabilities of quantum computers…

How do secondary school teachers’ professional learning preferences relate to teaching experience and the school context?

Promotor: J. N. Kok, Co-promotor: A. J. Knobbe

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In the Department of Business Sciences, three chairs are established. Together, the chair holders lead the Turnaround, Rescue & Insolvency Leiden Research Team (TRI Leiden), which consists of legal and business science researchers from Leiden Law School. TRI Leiden aims, through an international and…

Music profession requires strong reflective, collaborative, creative and improvisational skills, yet prevailing one-to-one tuition in conservatoire education focuses mainly on transmission of craft skills. Examining effects of students' collaborative and experiential learning, as in learning labs, creates…

Learning from small data sets in machine learning is a crucial challenge, especially when dealing with data imbalances and anomaly detection. This thesis delves into the challenges and methodologies of learning from small datasets in machine learning, with a particular focus on addressing data imbalances…

With the dramatic growth of the image data on the web, there is an increasing demand of the algorithms capable of understanding the visual information automatically.

The invention of neural networks marks a critical milestone in the pursuit of true artificial intelligence. Despite their impressive performance on various tasks, these networks face limitations in learning efficiently as they are often trained from scratch.

The active learning network joins together everyone interested in the subject to move the theme further within Leiden University. The SALTSWAT pilot program researches the ways forward for Leiden University.

The active learning ambition is based on the idea that knowledge is more likely to ‘stick’ when students are actively engaged with their learning and research. This active student participation has implications for how we teach: less consumption of knowledge and more efficient use of contact hours.

The ambition to have flexible learning pathways is about creating possibilities to improve the content and form of students’ learning process, and to link learning to students’ needs. Students who have access to a flexible range of learning pathways can align their university career with their own personal…

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Computers are capable of making incredibly accurate predictions on the basis of machine learning. In other words, these computers can learn without intervention once they have been pre-programmed by humans. At LIACS, we explore and push the borders of what a revolutionary new generation of algorithms…

The Centre for Professional Learning (CPL) develops in-depth and challenging programmes for higher educated professionals.

How does FoxP1 affect auditory perception on a behavioural and genomic level?

The language learning resource centre unites all language teaching professionals working at Leiden University: teachers and researchers at the LUCL, ATC, LUCAS, LIAS, and ICLON.

The ‘Leer te Durven!’ program (Learn to Dare) is a preventive training program for children with mild anxiety symptoms (Simon & Bögels, 2014). The program has been developed for children between the ages of 8 and 12 who feel or behave anxiously, avoid situations, are afraid of doing things wrong, appear…

LLInC supports innovative and high-quality education, within Leiden University and in partnership with academic and social organisations.

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Leiden archaeologists investigate how people in the past impacted their environment. Together with scientists, environmental scientists, and humanities experts, they use this information to draw conclusions about the present – and show what we can learn from it for the future.

The aim of this study was to provide insight into how teachers in higher education can be supported in the design, implementation and evaluation of group assignments by developing a theoretical and evidence-based framework for the design of group assignments.

Assessing Learning in Higher Education addresses what is probably the most time-consuming part of the work of staff in higher education, and something to the complexity of which many of the recent developments in higher education have added. Getting assessment ‘right’– that is, designing and implementing…

News, inspiration, background information and more about the Learning@LeidenUniversity vision on teaching and learning.

You may have to complete forms or have documents signed upon your arrival at Leiden University. Find out who you can turn to for assistance.

Students at Leiden University College (LUC) gain an understanding of journalism ‘from the inside out’ through innovative teaching methods in the courses Multimedia Journalism, Investigative Journalism and Gender, Media and Conflict. By simulating the actual daily tasks of journalists, they are exposed…

This thesis mainly focuses on multimodal understanding and Visual Question Answering (VQA) via deep learning methods. For technical contributions, this thesis first focuses on improving multimodal fusion schemes via multi-stage vision-language interactions.

In this thesis, deep learning is studied from a statistical perspective. Convergence rates for the worst case risk bounds of neural network estimators are obtained in the classification, density estimation and linear regression model.

Heightened activation of the striatum that adolescents show in response to reward is often associated with risk-taking and negative health consequences. This article in Nature Communications investigates a potential positive side of this heightened activation. It shows that the activity peak in late…

In 2008, the intervention ‘Denken + Doen = Durven’ (Think + Do = Dare) was developed in the Netherlands (Bögels, 2008). This is a cognitive behavioral therapy program for children and young people between the age of 8 to 18, diagnosed with an anxiety disorder. The intervention is given by a professional,…

This thesis mainly focuses on cross-modal retrieval and single-modal image retrieval via deep learning methods, i.e. by using deep convolutional neural networks.

In deep reinforcement learning, searching and learning techniques are two important components. They can be used independently and in combination to deal with different problems in AI, and have achieved impressive results in game playing and robotics. These results have inspired research into artificial…

How do museum visitors interpret the authenticity of museum objects? How can we support visitors' meaningful interactions with real objects?

This dissertation is about Bayesian learning from data. How can humans and computers learn from data?

The main interest of this project concerns learners’ perceived learning outcomes in massive open online courses and the factors related to their perceived learning outcomes.

Within the Vision on Teaching and Learning, one of the 8 ambitions is Activating Teaching and Learning. With the support of the Vice-Deans and Vice-Rector, Hester Bijl, the Saltswat project is working on accelerating this ambition. What is the purpose of this site? Being effective…

The research project focuses on teachers’ learning motivation and learning performance in professional learning activities.

The Learning and Behavior Problems in Education program group is responsible for teaching in the area of learning problems.

Here you will find an overview of the Spaces for Active Teaching and Learning already implemented at Leiden University and LUMC. These rooms vary in size, location, material affordances, and technological affordances, and thus vary in the forms of pedagogy they best support. You can use this site as…

This master specialization offers you a combination of theories about learning problems and impairments and intervention and prevention strategies from a developmental perspective.

2 joint initiatives began in 2019 which attempt to connect the various efforts around active learning at Leiden University: the active learning network, and the Saltswat project.

In a rapidly changing world it is crucial that our teaching keeps pace with the dynamics of society.

You can get various forms of support for you active learning ambitions. Have you already reflected on your teaching practice and have a clear idea of your goals? Your first point of contact should then be your ICTO Coordinator & ICLON. Do you want to join the network of others involved in the topic?…

The Learning@LeidenUniversity vision on teaching and learning is about what we want our future graduates to be and what that means for our curricula and our learning environment. It offers a long-term framework for the further development of our education and marks the start of a process of innovati…

The research of the group Learning and Behavior Problems in Education addresses learning difficulties in the areas of reading (technical reading and reading comprehension) math and writing, and examines learning-related behaviors behaviors.

Development of effective VR training for International Law of Armed Conflict (ILAC)