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International audience; Digital catalogues must be intuitive and easy to use. However, designing their interfaces is a complex task because there is so much available information and such little space. The choice of search filters, their format, their position, including the way to represent the results, are not trivial decisions. This paper presents the User-Driven Interface Design (UDID) method that offers five steps with specific material to help end users produce mock-up interfaces for digital catalogues. This method recommends letting participants compose their interfaces according to their needs. In this article, we present how the UDID method offered several befits for designing the interface of a Learning Game catalogue. 17 participants followed this method to produce five mock-up interfaces that we then analysed and compared to create the final interface.

International audience; Biometrics authentication is now widely deployed, and from that omnipresence comes the necessity to protect private data. Recent studies proved touchscreen handwritten digits to be a reliable biomet-rics. We set a threat model based on that biometrics: in the event of theft of unlabelled embeddings of handwritten digits, we propose a labelling method inspired by recent unsupervised translation algorithms. Provided a set of unlabelled embeddings known to have been produced by a Long Short Term Memory Recurrent Neural Network (LSTM RNN), we demonstrate that inferring their labels is possible. The proposed approach involves label-wise clustering of the embeddings and label identification of each group by matching their distribution to the label-relative classes of a comparison hand-crafted labeled set of embeddings. Cluster labelling is done through a two steps process including a genetic algorithm that finds the N-best matching hypotheses before a fine-tuning of those N-candidates. The proposed method was able to infer the correct labels on 100 randomised runs on different dataset splits.

In this paper, we propose several specific embeddings in Arabic sentiment analysis (SA) framework. Indeed, Arabic is characterized by its agglutination and morphological richness contributing to great sparsity that could affect embedding quality. This work presents a rigorous study that compares different types of Arabic-specific embeddings. We evaluate them with 2 neural architectures: one based on convolutional neural network (CNN) and the other one based on Bidirectional Long Short-Term Memory Bi-LSTM. Experiments are done on the Large Arabic-Book Reviews corpus LABR. Our best results boost previous published accuracy by 1.9%. Moreover, we experiment combination of our individual systems defining very confident decision, reaching an accuracy of 92.2% on 98.25% of LABR test dataset.

This paper presents the ongoing conception of a set of tools, based on automatic speech recognition for capturing communications in educational context in real time and to offer results to learners instantly. This concept is integrated in an environment to support learners in blended contexts. Its goal is to help students stay focus on the teacher’s explanations and offer them greater possibilities of interactions.

International audience; In a learning situation, gamification is a way to motivate learners and enhance their participation to learning activities by adding game elements. But it still pays little attention to the individual differences among learners’ preferences as players. This paper presents a generic and adaptive gamification system that can be plugged on various learning environments. The game elements can be automatically adapted, based on an analysis of the interaction traces. The architecture of the proposed system is based on the separation between the control of the pedagogical elements and the control of the game elements. To support the adaptivity of the game elements, we refer to a user model based on a list of player types. The user model is initialized thanks to the traces of interactions, and finally used to predict which game feature will be relevant for the user.

International audience; Many learning environments are swiftly abandoned by the learners, even if they are effective. Gamification is as a recent game-based learning approach that can enhance the learners’ motiva-tion. However, individual expectations and preferences towards game-like features may be very different from one person to another. This paper presents a model to adapt gamification features according to a player profile of the learners. Two version of this model are evaluated within a gamified online learning environment. The first version comes from experts’ judgment, and the second one is induced from empirical data. Our experiments confirm that the first version can be efficient to predict the player’s preferences among the gamification features.