466 search results for “boender learning” in the Public website
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Carolien Boender
Faculty of Humanities
c.boender@hum.leidenuniv.nl | +31 71 527 1646
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Yolinde van Paridon
Faculteit der Sociale Wetenschappen
y.van.paridon.3@fsw.leidenuniv.nl | +31 71 527 1488
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Teachers’ professional learning preferences
How do secondary school teachers’ professional learning preferences relate to teaching experience and the school context?
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Learning labs in conservatoire education
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…
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Computational speedups and learning separations in quantum machine learning
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…
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Massively collaborative machine learning
Promotor: J. N. Kok, Co-promotor: A. J. Knobbe
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Andreas Sauter
Science
a.w.m.sauter@liacs.leidenuniv.nl | +31 71 527 4799
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Andreas Paraskeva
Science
a.paraskeva@liacs.leidenuniv.nl | +31 71 527 2727
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Nonverbal Learning Disorder (NLD)
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Deep learning for visual understanding
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.
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Understanding deep meta-learning
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.
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Sleep and learning in children
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Emma Everaert
Faculteit der Sociale Wetenschappen
e.everaert@fsw.leidenuniv.nl | +31 71 527 2727
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Kim Stroet
Faculteit der Sociale Wetenschappen
k.f.a.stroet@fsw.leidenuniv.nl | +31 71 527 3955
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Julia Wasala
Science
j.wasala@liacs.leidenuniv.nl | +31 71 527 4799
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Siuman Chung
Faculteit der Sociale Wetenschappen
s.chung@fsw.leidenuniv.nl | +31 71 527 3830
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Christine Espin
Faculteit der Sociale Wetenschappen
espinca@fsw.leidenuniv.nl | +31 71 527 6630
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Marit Guda
Faculteit der Sociale Wetenschappen
m.c.guda@fsw.leidenuniv.nl | +31 71 527 6344
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Michael Lew
Science
m.s.lew@liacs.leidenuniv.nl | +31 71 527 7034
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Elise Swart
Faculteit der Sociale Wetenschappen
e.k.swart@fsw.leidenuniv.nl | +31 71 527 2727
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Collaborative learning in higher education: design, implementation and evaluation of group learning activities
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.
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Professional learning: what teachers want to learn
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?
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Exploring deep learning for multimodal understanding
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.
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Collaborative learning in conservatoire education: catalyst for innovation
The aim of this research project was to increase understanding of which collaborative learning approaches already exist in conservatoire education, and how implementation of collaborative learning could be supported.
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Increased striatal activity in adolescence benefits learning
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…
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Reinforcement learning
The Reinforcement Learning lab conducts research into Reinforcement Learning and Intelligent Combinatorial Algorithms.
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Machine Learning
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…
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Blended learning
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
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Exploring Deep Learning for Intelligent Image Retrieval
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.
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Nurbolat Kenbayev
Science
n.kenbayev@liacs.leidenuniv.nl | +31 71 527 2727
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Self-directed learning with mobile technology in higher education
Language learners in higher education increasingly conduct out-of-class self-directed learning facilitated by mobile technology. This project aims to explore how university students use mobile technology for their self-directed language learning and investigate factors that influence their self-directed…
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Collaborative learning in teacher education: Intended, implemented and experienced curriculum
How is collaborative learning in teacher education designed and implemented? How do students experience those collaborative learning assignments? What aspects of the design and the implementation lead to which perceived learning outcomes?
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Student engagement in blended learning in higher education
In what way can teachers support and enlarge student engagement in a blended learning context?
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Wilfried Admiraal
ICLON
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Frans Rodenburg
Science
f.j.rodenburg@biology.leidenuniv.nl | +31 71 527 2727
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Dineke Tigelaar
ICLON
dtigelaar@iclon.leidenuniv.nl | +31 71 527 6552
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Beth Lloyd
Faculteit der Sociale Wetenschappen
b.lloyd@fsw.leidenuniv.nl | +31 71 527 2727
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Learning from small samples
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…
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University teachers’ learning paths during technological innovation of education
To what extent are university teachers' individual learning paths influenced by their teaching experience, motivation, and conceptions of teaching and learning through educational technology?
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Active Learning Network
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.
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Activating teaching and learning
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.
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Flexible learning pathways
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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Artificial Intelligence & Machine Learning
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…
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Centre for Professional Learning
The Centre for Professional Learning (CPL) develops in-depth and challenging programmes for higher educated professionals.
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Neurogenomics of vocal learning
How does FoxP1 affect auditory perception on a behavioural and genomic level?
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Object-based learning in science museums
How do museum visitors interpret the authenticity of museum objects? How can we support visitors' meaningful interactions with real objects?
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Machine learning and computer vision for urban drainage inspections
Sewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections for defects are performed.
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Language Learning Resource Centre
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.
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Searching by Learning: Exploring Artificial General Intelligence on Small Board Games by Deep Reinforcement Learning
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…
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Learn to Dare!
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…