University of Rennes

About: University of Rennes is a education organization based out in Rennes, France. It is known for research contribution in the topics: Population & Crystal structure. The organization has 18404 authors who have published 40374 publications receiving 995327 citations.

Digital game-based learning: Impact of instructions and feedback on motivation and learning effectiveness

Abstract: Although many studies have investigated the effects of digital game-based learning (DGBL) on learning and motivation, its benefits have never been systematically demonstrated. In our first experiment, we sought to identify the conditions under which DGBL is most effective, by analyzing the effects of two different types of instructions (learning instruction vs. entertainment instruction). Results showed that the learning instruction elicited deeper learning than the entertainment one, without impacting negatively on motivation. In our second experiment, we showed that if learners are given regular feedback about their performance, the entertainment instruction results in deep learning. These two experiments demonstrate that a serious game environment can promote learning and motivation, providing it includes features that prompt learners to actively process the educational content. We examined the effects of two types of instructions on digital game-based learning.Experiment 1 showed that learning is deeper with a learning instruction.Experiment 2 showed that an entertainment instruction with feedback is beneficial.These results are discussed in terms of motivation and learning processes.

Increased exonic de novo mutation rate in individuals with schizophrenia

Abstract: Schizophrenia is a severe psychiatric disorder that profoundly affects cognitive, behavioral and emotional processes. The wide spectrum of symptoms and clinical variability in schizophrenia suggest a complex genetic etiology, which is consistent with the numerous loci thus far identified by linkage, copy number variation and association studies. Although schizophrenia heritability may be as high as ∼80%, the genes responsible for much of this heritability remain to be identified. Here we sequenced the exomes of 14 schizophrenia probands and their parents. We identified 15 de novo mutations (DNMs) in eight probands, which is significantly more than expected considering the previously reported DNM rate. In addition, 4 of the 15 identified DNMs are nonsense mutations, which is more than what is expected by chance. Our study supports the notion that DNMs may account for some of the heritability reported for schizophrenia while providing a list of genes possibly involved in disease pathogenesis.

Unsupervised terrain classification preserving polarimetric scattering characteristics

Abstract: In this paper, we proposed an unsupervised terrain and land-use classification algorithm using polarimetric synthetic aperture radar data. Unlike other algorithms that classify pixels statistically and ignore their scattering characteristics, this algorithm not only uses a statistical classifier, but also preserves the purity of dominant polarimetric scattering properties. This algorithm uses a combination of a scattering model-based decomposition developed by Freeman and Durden and the maximum-likelihood classifier based on the complex Wishart distribution. The first step is to apply the Freeman and Durden decomposition to divide pixels into three scattering categories: surface scattering, volume scattering, and double-bounce scattering. To preserve the purity of scattering characteristics, pixels in a scattering category are restricted to be classified with other pixels in the same scattering category. An efficient and effective class initialization scheme is also devised to initially merge clusters from many small clusters in each scattering category by applying a merge criterion developed based on the Wishart distance measure. Then, the iterative Wishart classifier is applied. The stability in convergence is much superior to that of the previous algorithm using the entropy/anisotropy/Wishart classifier. Finally, an automated color rendering scheme is proposed, based on the classes' scattering category to code the pixels to resemble their natural color. This algorithm is also flexible and computationally efficient. The effectiveness of this algorithm is demonstrated using the Jet Propulsion Laboratory's AIRSAR and the German Aerospace Center's (DLR) E-SAR L-band polarimetric synthetic aperture radar images.