Classification and Regression of Learner’s Scores in Logic Environment

This paper presents the possibility of classifying and regressing learner’s scores according to different cognitive tasks which are grouped with difficulty level, type and category. This environment is namely, Logic environment. It is mainly divided into three main categories: memory, concentration and reasoning. To classify and regress learner’s scores according to the category and the type of cognitive task acquired, we trained and tested different machine learning algorithms such as linear regression, support vector machines, random forests and gradient boosting. Primary results shows that a random forest algorithm is the most suitable model for classifying and regressing the learners’ scores in cognitive tasks, where the features most important for the model are, in descending order: the task difficulty and the task category in the case of regression, the task difficulty, the time taken by the participant before completing it, and his electroencephalogram mental metrics in the case of classification

An Integrated Approach for Automatic\ud Aggregation of Learning Knowledge Objects

This paper presents the Knowledge Puzzle, an ontology-based platform designed to facilitate domain\ud knowledge acquisition from textual documents for knowledge-based systems. First, the\ud Knowledge Puzzle Platform performs an automatic generation of a domain ontology from documents’\ud content through natural language processing and machine learning technologies. Second,\ud it employs a new content model, the Knowledge Puzzle Content Model, which aims to model\ud learning material from annotated content. Annotations are performed semi-automatically based\ud on IBM’s Unstructured Information Management Architecture and are stored in an Organizational\ud memory (OM) as knowledge fragments. The organizational memory is used as a knowledge\ud base for a training environment (an Intelligent Tutoring System or an e-Learning environment).\ud The main objective of these annotations is to enable the automatic aggregation of Learning\ud Knowledge Objects (LKOs) guided by instructional strategies, which are provided through\ud SWRL rules. Finally, a methodology is proposed to generate SCORM-compliant learning objects\ud from these LKOs

LewiSpace: an Exploratory Study with a Machine Learning Model in an Educational Game

The use of educational games as a tool for providing learners with a playful and educational aspect is widespread. In this paper, we present an educational game that we developed to teach a chemistry lesson, namely drawing a Lewis diagram. Our game is a 3D environment known as LewiSpace and aims at balancing between playful and educational contents in order to increase engagement and motivation while learning. The game contains mainly five different missions aim at constructing Lewis diagram molecules which are organized in an ascending order of difficulty. We also conducted an experiment to gather data about learners’ cognitive and emotional states as well as their behaviours through our game by using three types of sensors (electroencephalography, eye tracking, and facial expression recognition with an optical camera) and a self report personality questionnaire (the Big Five). Primary results show that a machine learning model namely logistic regression, can predict with some success whether the learner will success or fail in each mission of our game, and paves the way for an adaptive version of the game. This latter will challenge or assist learners based on some features extracted from our data. Feature extraction integrated into a machine learning model aims mainly at providing learners’ with a real-time adaptation according to their performance and skills while progressing in our game

Assessment of Learners’ Motivation during Interactions with Serious Games: A Study of Some Motivational Strategies in Food-Force

This study investigated motivational strategies and the assessment of learners’ motivation during serious gameplay. Identifying and intelligently assessing the effects that these strategies may have on learners are particularly relevant for educational computer-based systems. We proposed, therefore, the use of physiological sensors, namely, heart rate, skin conductance, and electroencephalogram (EEG), as well as a theoretical model of motivation (Keller’s ARCS model) to evaluate six motivational strategies selected from a serious game called Food-Force. Results from nonparametric tests and logistic regressions supported the hypothesis that physiological patterns and their evolution are suitable tools to directly and reliably assess the effects of selected strategies on learners’ motivation. They showed that specific EEG “attention ratio” was a significant predictor of learners’ motivation and could relevantly evaluate motivational strategies, especially those associated with the Attention and Confidence categories of the ARCS model of motivation. Serious games and intelligent systems can greatly benefit from using these results to enhance and adapt their interventions

A Pragma-Semantic Analysis of the Emotion/Sentiment Relation in Debates

International audienceIn the last years, emotions recognition tools have become more and more popular, aiming at detecting the emotions of human actors while performing different intelligent tasks by means of headsets and facial emotions detection tools. In addition to this kind of technology , when participants interact with each others by means of tex-tual exchanges, sentiment analysis techniques, from the natural language processing research area, are exploited to detect the polarity of the exchanged messages. Investigating how these two connected components interacts and can support each other towards a better emotions and sentiment detection is a relevant but unexplored research challenge. In this paper, we start from a dataset of debate interactions annotated with the emotions of the involved participants, captured by means of EEG headsets and a facial emotions recognition tool, and the argumentative structures of the debates, and we compare this information to the polarity of the proposed textual arguments, retrieved through a sentiment analysis algorithm. A pragma-semantic analysis of the obtained results is provided, along with a discussion of the potential future work

Interactive Point-based Modeling of Complex Objects from Images

International audienceModeling complex realistic objects is a difficult and time consuming process. Nevertheless, with improvements in rendering speed and quality, more and more applications require such realistic complex 3D objects. We present an interactive modeling system that extracts 3D objects from photographs. Our key contribution lies in the tight integration of a point-based representation and user interactivity, by introducing a set of interactive tools to guide reconstruction. 3D color points are a flexible and effective representation for very complex objects; adding, moving, or removing points is fast and simple, facilitating easy improvement of object quality. Because images and depths maps can be very rapidly generated from points, testing validity of point projections in several images is efficient and simple. These properties allow our system to rapidly generate a first approximate model, and allow the user to continuously and interactively guide the generation of points, both locally and globally. A set of interactive tools and optimizations are introduced to help the user improve the extracted objects

Actes de la 9ème conférence des Technologies de l’Information et de la Communication pour l’Enseignement (TICE 2014)

National audienceLe cycle de conférence TICE a pour objectif de faire tous les deux ans le point sur les résultats de recherches, les nouvelles applications, les derniers usages, et les retours d’expériences dans le domaine de l’éducation supérieure numérique. Le colloque TICE 2014 est organisé par l’IUT de Beziers, une composante de l’Université Montpellier 2. Cette neuvième édition du colloque TICE sera l’occasion de rassembler à Béziers, du 18 au 20 Novembre 2014, la communauté scientifique et industrielle des TICE autour du thème « Nouvelles pédagogies et sciences et technologies du numérique »

Découplage de la structure et de l'apparence pour la synthèse de détail par l'exemple

One of the goals of computer graphics is to produce realistic images. To this end, artists build complex geometric models covered by detailed textures. Unfortunately, depending on the target application and the available resources, they often have to "take shortcuts" by repeating textures or using coarse models whose details are simulated by a series of appearance layers. In order to create realistic 3D objects, the best inspiration source is the real world itself. In this context, we propose to help the artiste create these appearance layers by analyzing given examples. We first address the color texture layer, and in particular, structured textures. Indeed, these textures are hardly reproducible automatically and their visual repetition is annoying for an observer. In order to succeed in propagating these kinds of textures using a given sample, we propose to decouple the structure from the appearance.We first handle the problem under a geometric aspect. The structured textures are represented by a 2D mesh which we analyze in order to extract a compact statistical description. This leads to the creation of a generator able to reproduce the input structure at will. The user is taken into account all along the process and can influence the result through the use of constraint maps. In parallel, we analyze the appearance of the structured texture in order to extract a small set of textures representing the materials used for the texture regions and their contour. This information allows us to efficiently texture any given synthesized structure.In a third step, we use the texture and structure information, alongside additional user-provided input inorder to generate and propagate 3D details onto models. We propose a generic framework which should allow to relieve the artists from repetitive and tedious tasks. We apply this framework to the synthesis of displacement maps and present other application cases.The set of techniques described allows to synthesize detailed structured textures from example, with a reduced memory usage and allowing for real-time rendering, while letting the user have control over various parts of the process.Un des buts de l'infographie est de produire des images réalistes. Pour cela, des artistes créent des modèles géométriques complexes ornés de textures détaillées. Malheureusement, selon l'application visée et les ressources disponibles, il doivent souvent "prendre des raccourcis" en répétant des textures ou en utilisant des modèles simples dont les détails sont simulés par une série de couches d'apparence. Afin de créer des objets 3D réalistes, la meilleure source d'inspiration est le monde réel lui-même. Dans ce contexte, nous proposons d'aider l'artiste à créer ces couches d'apparence à partir de l'analyse d'exemples. Nous nous attaquons tout d'abord à la couche de texture couleur, et plus particulièrement aux textures structurées.En effet, ces dernières sont difficilement reproductibles de manière automatique et leur répétition visuelle est gênante pour l'observateur. Afin de réussir à propager de telles textures à partir d'un exemplaire donné, nous proposons de découpler la structure de l'apparence.Nous traitons le problème tout d'abord sous un aspect géométrique. Les textures structurées sont représentées par un maillage 2D qui est analysé afin d'en extraire une description statistique compacte menant à la création d'un générateur permettant de reproduire la structure originelle à volonté. L'utilisateur est pris en compte tout au long du processus et peut influencer le résultat grâce à des cartes de contraintes.En parallèle, nous analysons l'apparence de la texture structurée afin d'extraire un ensemble réduit de textures représentant les matériaux utilisés pour l'intérieur des régions et leur contour. Ces informations nous permettent d'"habiller" de nouvelles structures de manière efficace. Dans un troisième temps, nous utilisons l'information de structure et de texture, ainsi que des informations usager afin de générer et propager des détails 3D sur des modèles. Nous proposons un système générique qui permettra d'alléger les artistes de tâches souvent répétitives et fastidieuses. Nous appliquons ce système à la génération de cartes de hauteurs et présentons plusieurs autres cas d'application.L'ensemble des techniques proposées permet de synthétiser des textures structurées détaillées à partir d'exemples avec un coût mémoire réduit et permettant un rendu en temps-réel, tout en laissant du contrôle à l'utilisateur