Fostering argumentation-based computer-supported collaborative learning in higher education

cum laude graduation (with distinction). In collaborative settings, students of all ages need to learn to clearly explain their informed opinions and give reasons for the way in which they carry out tasks and solve problems. Engaging students in collaborative discussion and argumentation is an educational approach for preparing them to manage today’s complex issues and actively participate in knowledge societies. Despite the fact that argumentation is shaped in social conversation and also in learners’ online exchanges in daily life, learners in academic settings need to be taught to reason and argue in a way that is beneficial for knowledge sharing, domain-specific learning, and knowledge construction. Online support systems for collaboration or Computer-Supported Collaborative Learning (CSCL) environments in which learners argue in teams have been found to support the sharing, constructing, and representing of arguments with the aim of learning. This type of learning arrangement is called Argumentation-Based Computer-Supported Collaborative Learning (ABCSCL) and it is seen as a promising environment in which to facilitate collaborative argumentation and learning

Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment

This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs. online discussion) in computer-supported collaborative learning (CSCL) on student knowledge construction. Students (N = 87) were first randomly assigned to the two information presentation conditions to work individually on a case-based assignment in IDLM. Students who received information during learning task clusters tended to show better results on knowledge construction than those who received information only before each cluster. The students within the two separate information presentation conditions were then randomly assigned to pairs to discuss the outcomes of their assignments under either the personal discussion or online discussion condition in CSCL. When supportive information had been presented before each learning task cluster, online discussion led to better results than personal discussion. When supportive information had been presented during the learning task clusters, however, the online and personal discussion conditions had no differential effect on knowledge construction. Online discussion in CSCL appeared to compensate for suboptimal timing of presentation of supportive information before the learning task clusters in IDLM

Unsupervised Holistic Image Generation from Key Local Patches

We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior. In this work, key local patches are defined as informative regions of the target object or scene. This is a challenging problem since it requires generating realistic images and predicting locations of parts at the same time. We construct adversarial networks to tackle this problem. A generator network generates a fake image as well as a mask based on the encoder-decoder framework. On the other hand, a discriminator network aims to detect fake images. The network is trained with three losses to consider spatial, appearance, and adversarial information. The spatial loss determines whether the locations of predicted parts are correct. Input patches are restored in the output image without much modification due to the appearance loss. The adversarial loss ensures output images are realistic. The proposed network is trained without supervisory signals since no labels of key parts are required. Experimental results on six datasets demonstrate that the proposed algorithm performs favorably on challenging objects and scenes.Comment: 16 page

Cross Pixel Optical Flow Similarity for Self-Supervised Learning

We propose a novel method for learning convolutional neural image representations without manual supervision. We use motion cues in the form of optical flow, to supervise representations of static images. The obvious approach of training a network to predict flow from a single image can be needlessly difficult due to intrinsic ambiguities in this prediction task. We instead propose a much simpler learning goal: embed pixels such that the similarity between their embeddings matches that between their optical flow vectors. At test time, the learned deep network can be used without access to video or flow information and transferred to tasks such as image classification, detection, and segmentation. Our method, which significantly simplifies previous attempts at using motion for self-supervision, achieves state-of-the-art results in self-supervision using motion cues, competitive results for self-supervision in general, and is overall state of the art in self-supervised pretraining for semantic image segmentation, as demonstrated on standard benchmarks

Video Representation Learning by Recognizing Temporal Transformations

We introduce a novel self-supervised learning approach to learn representations of videos that are responsive to changes in the motion dynamics. Our representations can be learned from data without human annotation and provide a substantial boost to the training of neural networks on small labeled data sets for tasks such as action recognition, which require to accurately distinguish the motion of objects. We promote an accurate learning of motion without human annotation by training a neural network to discriminate a video sequence from its temporally transformed versions. To learn to distinguish non-trivial motions, the design of the transformations is based on two principles: 1) To define clusters of motions based on time warps of different magnitude; 2) To ensure that the discrimination is feasible only by observing and analyzing as many image frames as possible. Thus, we introduce the following transformations: forward-backward playback, random frame skipping, and uniform frame skipping. Our experiments show that networks trained with the proposed method yield representations with improved transfer performance for action recognition on UCF101 and HMDB51.Comment: ECCV 202

The dynamic elastic response to impulse synchronisation of composite prosthetic energy storing and returning feet

In this research, it is proven that perfect synchronisation between the input impulse (human effort) and the ground reaction impulse/impact can result in a phenomenon called the dynamic elastic response to impulse synchronisation with favourable consequences to the behaviour of the energy storing and returning foot. An energy storing and returning composite prosthetic foot was first used in elite competition at the 1988 Paralympic Games. Since its introduction, the prostheses have proven to be a source of controversy. In 2008, it was concluded that the athletes who use such technology when running are in advantageous position when compared to able-bodied athletes. However, these findings have since been disputed. There still remain unanswered questions regarding the level of contribution of energy storing and returning feet in the performance of amputee athletes. A series of investigations have previously been conducted to study the dynamic characteristics of a number of energy storing and returning composite prosthetic feet. It was found that if a sinusoidal input impulse, with a frequency close to one of the energy storing and returning feet’s natural frequencies, could be applied, it can make the energy storing and returning foot susceptible to resonance, which, in the case of bending mode, if sustained can lead to a gain in height or increased velocity

Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project \emph{Gravity Spy} has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program