Towards Industrialized Conception and Production of Serious Games

Serious Games (SGs) have experienced a tremendous outburst these last years. Video game companies have been producing fun, user-friendly SGs, but their educational value has yet to be proven. Meanwhile, cognition research scientist have been developing SGs in such a way as to guarantee an educational gain, but the fun and attractive characteristics featured often would not meet the public's expectations. The ideal SG must combine these two aspects while still being economically viable. In this article, we propose a production chain model to efficiently conceive and produce SGs that are certified for their educational gain and fun qualities. Each step of this chain will be described along with the human actors, the tools and the documents that intervene

Clustering and Analysis of User Motions to Enhance Human Learning: A First Study Case with the Flip Bottle Challenge

International audienceMore and more domains such as industry, sport, medicine, Human Computer Interaction (HCI) and education analyze user motions to observe human behavior, follow and predict its action, intention and emotion, to interact with computer systems and enhance user experience in Virtual (VR) and Augmented Reality (AR). In the context of human learning of movements, existing software applications and methods rarely use 3D captured motions for pedagogical feedback. This comes from several issues related to the highly complex and dimensional nature of these data, and by the need to correlate this information with the observation needs of the teacher. Such issues could be solved by the use of machine learning techniques, which could provide efficient and complementary feedback in addition to the expert advice, from motion data. The context of the presented work is the improvement of the human learning process of a motion, based on clustering techniques. The main goal is to give advice according to the analysis of clusters representing user profiles during a learning situation. To achieve this purpose, a first step is to work on the separation of the motions into different categories according to a set of well-chosen features. In this way, allowing a better and more accurate analysis of the motion characteristics is expected. An experimentation was conducted with the Bottle Flip Challenge. Human motions were first captured and filtered, in order to compensate for hardware related errors. Descriptors related to speed and acceleration are then computed, and used in two different automatic approaches. The first one tries to separate the motions, using the computed descriptors, and the second one, compares the obtained separation with the ground truth. The results show that, while the obtained partitioning is not relevant to the degree of success of the task, the data are separable using the descriptors

A Web-based System for Observing and Analyzing Computer Mediated Communications

Tracking data of user's activities resulting from Computer Mediated Communication (CMC) tools (forum, chat, etc.) is often carried out in an ad-hoc manner, which either confines the reusability of data in different purposes or makes data exploitation difficult. Our research works are biased toward methodological challenges involved in designing and developing a generic system for tracking user's activities while interacting with asynchronous communication tools like discussion forums. We present in this paper, an approach for building a Web-based system for observing and analyzing user activity on any type of discussion forums

Rôles du tuteur

National audienceDans la littérature, le tuteur est nommé différemment, selon les rôles qu'on lui assigne : modérateur, facilitateur, tuteur en ligne, coach, mentor pédagogique, e-tuteur, accompagnateur... Dans cet article, nous nous attachons à mieux définir l'acteur de l' apprentissage que l' on nomme " tuteur " en étudiant ses rôles en fonction de trois facteurs : la médiatisation des communications (médiatisées ou non), la nature de la tâche (collaborative ou individuelle) et la temporalité de la formation (ponctuelle ou durable). Nous relevons ainsi 16 rôles dont la présence dépend de l'importance de chacun de ces facteurs. Ce travail a été réalisé dans le but d'identifier les difficultés et problèmes rencontrés par le tuteur en fonction du contexte de la formation, ses besoins et l'état actuel des outils existants pour y répondre

Retrieval and Registration of Long-Range Overlapping Frames for Scalable Mosaicking of In Vivo Fetoscopy

Purpose: The standard clinical treatment of Twin-to-Twin Transfusion Syndrome consists in the photo-coagulation of undesired anastomoses located on the placenta which are responsible to a blood transfer between the two twins. While being the standard of care procedure, fetoscopy suffers from a limited field-of-view of the placenta resulting in missed anastomoses. To facilitate the task of the clinician, building a global map of the placenta providing a larger overview of the vascular network is highly desired. Methods: To overcome the challenging visual conditions inherent to in vivo sequences (low contrast, obstructions or presence of artifacts, among others), we propose the following contributions: (i) robust pairwise registration is achieved by aligning the orientation of the image gradients, and (ii) difficulties regarding long-range consistency (e.g. due to the presence of outliers) is tackled via a bag-of-word strategy, which identifies overlapping frames of the sequence to be registered regardless of their respective location in time. Results: In addition to visual difficulties, in vivo sequences are characterised by the intrinsic absence of gold standard. We present mosaics motivating qualitatively our methodological choices and demonstrating their promising aspect. We also demonstrate semi-quantitatively, via visual inspection of registration results, the efficacy of our registration approach in comparison to two standard baselines. Conclusion: This paper proposes the first approach for the construction of mosaics of placenta in in vivo fetoscopy sequences. Robustness to visual challenges during registration and long-range temporal consistency are proposed, offering first positive results on in vivo data for which standard mosaicking techniques are not applicable.Comment: Accepted for publication in International Journal of Computer Assisted Radiology and Surgery (IJCARS

SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data

State-of-the-art face recognition networks are often computationally expensive and cannot be used for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets. Meanwhile, there are privacy and ethical concerns with collecting and using large face recognition datasets. While generating synthetic datasets for training face recognition models is an alternative option, it is challenging to generate synthetic data with sufficient intra-class variations. In addition, there is still a considerable gap between the performance of models trained on real and synthetic data. In this paper, we propose a new framework (named SynthDistill) to train lightweight face recognition models by distilling the knowledge of a pretrained teacher face recognition model using synthetic data. We use a pretrained face generator network to generate synthetic face images and use the synthesized images to learn a lightweight student network. We use synthetic face images without identity labels, mitigating the problems in the intra-class variation generation of synthetic datasets. Instead, we propose a novel dynamic sampling strategy from the intermediate latent space of the face generator network to include new variations of the challenging images while further exploring new face images in the training batch. The results on five different face recognition datasets demonstrate the superiority of our lightweight model compared to models trained on previous synthetic datasets, achieving a verification accuracy of 99.52% on the LFW dataset with a lightweight network. The results also show that our proposed framework significantly reduces the gap between training with real and synthetic data. The source code for replicating the experiments is publicly released.Comment: Accepted in the IEEE International Joint Conference on Biometrics (IJCB 2023

Evaluating Learning Games during their Conception

International audienceLearning Games (LGs) are educational environments based on a playful approach to learning. Their use has proven to be promising in many domains, but is at present restricted by the time consuming and costly nature of the developing process. In this paper, we propose a set of quality indicators that can help the conception team to evaluate the quality of their LG during the designing process, and before it is developed. By doing so, the designers can identify and repair problems in the early phases of the conception and therefore reduce the alteration phases, that occur after testing the LG's prototype. These quality indicators have been validated by 6 LG experts that used them to assess the quality of 24 LGs in the process of being designed. They have also proven to be useful as design guidelines for novice LG designers

Modeling and Evaluating of Human 3d+t Activities in Virtual Environment

International audienceThis paper studies the problem of evaluation of human 3d+t activities in Virtual Environments (VE) for Learning (VEL). Current evaluation methods focus mostly on: (i) the automatic identification of an ordered sequence of actions and/or (ii), an empirical analysis made by experts through the VE. In many cases, the learner's activity can be represented by some specific time series made of geometrical data of 3D artefacts. For the extraction and analysis of such Motions Of Interest (MOI), one can manually segment them among the traces, and/or use automatic approaches requiring a database of annotated examples. Both cases usually require too many resources to design such environments. Consequently, this work presents a method allowing teachers to quickly build, compare and evaluate a 3d+t learning activity in VE. This method is based on a semi-automatic approach combining the Dynamic Time Warping algorithm, with 3D reference shapes and few expert's demonstrations of the task to learn

Lessons Learned from the Development of a Mobile Learning Game Authoring Tool

International audienceStudents and schools are increasingly equipped with smartphones and tablets. These mobile devices can enhance teaching in many ways. Mobile Learning Games (MLGs) for example, have shown great potential for increasing student's motivation and improving the quality of situated learning. For the past few years, the research community has been working on authoring tools that allow teachers to create and distribute their own MLGs. The development of these authoring tools is challenging and time consuming and even more so if the objective is for these tools to actually be used in classrooms. The Design-Based Research (DBR) paradigm was precisely developed to address these central issues of Technology Enhanced Learning. It involves co-designing and testing with end-users from the beginning of the project. Although DBR increases the acceptance of new educational tools, it also adds several challenges, including the complexity of involving teachers and students in real-world situations and creating several versions of the tools that will be improved iteratively. In this paper, we aim at providing design principles and practical guidance on the way to develop such authoring tools, based on our experience. We conclude on lessons learned from this project and discuss some systematic issues we faced