Publications related to Leveraging Learner Errors in Digital Argumentation Learning: How ALure Helps Students Learn from their Mistakes and Write Better Arguments

Related publications (9)

Towards the Detection of AI-Synthesized Human Face Images

Over the past years, image generation and manipulation have achieved remarkable progress due to the rapid development of generative AI based on deep learning. Recent studies have devoted significant efforts to address the problem of face image manipulation ...

2024

Modular Clinical Decision Support Networks (MoDN)-Updatable, interpretable, and portable predictions for evolving clinical environments

Cécile Claire Trottet, Martin Jaggi, Mary-Anne Hartley, Thijs Vogels

Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with probabilistic guidance. However, many CDSS deploy static, generic rule-based logic, resulting in inequitably distributed accuracy and inconsistent performance ...

PUBLIC LIBRARY SCIENCE2023

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

Beril Besbinar

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

EPFL2022

Pretraining boosts out-of-domain robustness for pose estimation

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Steffen Schneider

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...

IEEE COMPUTER SOC2021

Deep Generative Models and Applications

Tatjana Chavdarova

Over the past few years, there have been fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. The amount of annotated data drastically increased and supervised deep discriminative models exceed ...

EPFL2020

Improving speech embedding using crossmodal transfer learning with audio-visual data

Jean-Marc Odobez

Learning a discriminative voice embedding allows speaker turns to be compared directly and efficiently, which is crucial for tasks such as diarization and verification. This paper investigates several transfer learning approaches to improve a voice embeddi ...

2019

Using Mobile Eye-Trackers to Unpack the Perceptual Benefits of a Tangible User Interface for Collaborative Learning

Pierre Dillenbourg, Kshitij Sharma, Guillaume Zufferey, Sébastien Cuendet, Bertrand Roland Schneider

In this study, we investigated the way users memorize, analyze, collaborate, and learn new concepts on a Tangible User Interface (TUI). Twenty-seven pairs of apprentices in logistics (N = 54) interacted with an interactive simulation of a warehouse. Their ...

Assoc Computing Machinery2016

Application of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers

Philippe Müllhaupt, Ehsan Sarshari

Scour can have the effect of subsidence of the piers in bridges, which can ultimately lead to the total collapse of these systems. Effective bridge design needs appropriate information on the equilibrium depth of local scour. The flow field around bridge p ...

2015

Experiential learning approach for requirements engineering education

Alain Wegmann, Gil Regev

The use of requirements engineering (RE) in industry is hampered by a poor understanding of its practices and their benefits. Teaching RE at the university level is therefore an important endeavor. Shortly before students become engineers and enter the wor ...

Springer Verlag2009