International audience

International audience

The high failure rate is a common issue among online institutions. Early Warning Systems (EWSs) are widely adopted as a solution to deal with this issue. However, these systems do not go beyond the early identification of failing learners. In this paper, we propose a new alert algorithm of an educational EWS for generating risk alerts at the earliest. This algorithm is based on a weekly prediction model that aims to generate early alerts. The regular tracking of prediction results enabled to propose measures for the right prediction earliness and the model’s temporal stability. These measures prepare the last step of the algorithm which is the alerts generation according to a predefined rule. The objective of this rule is to target at-risk learners to improve their learning. For this aim, we used data of k-12 learners enrolled in an online physics-chemistry module.

The rapid and intense development of distance learning in recent years has led to increasingly comprehensive solutions, recording students’ activity in the form of learning traces. Adapted learning systems can exploit this data to analyze students’ behavior and help them be more successful in their learning process. In this paper, we propose an approach that is new in data mining, which consists of representing the behavioral dynamics of a set of students through Behavioral Primitives. A Behavioral Primitive represents the temporal evolution of learning indicators to model the behavior of a group of students. The primitive then corresponds to a dynamic distribution, representing both the mean values and the variance of specific indicators over time. We experiment this method on a known, widely used, and open dataset (OULAD). Our results confirm the relevance of the proposed methodology to characterize students’ behaviors intelligibly and visually while respecting each student's anonymity. Our work provides a powerful and explainable tool for educational actors. It allows them to analyze learners’ behaviors and perform pedagogical actions.

Syphilis is a Sexually Transmitted Infection (STI) which the Brazilian Ministry of Health has acknowledged an epidemic since 2016. To face such a problem, it is essential to develop and implement educational actions enhanced by information and communication technologies to qualify, train and raise awareness nationally. Considering the increasing number of Open Educational Resources developed, existing open health repositories and other digital platforms that allow interaction in the Brazilian Unified Health System (SUS) as well as the vast number of Healthcare Information Systems, it is essential to the develop solutions to efficiently and costly recommend content accordingly to the interest of health professionals and the current needs and priorities of the SUS, such as the epidemic of syphilis. This paper presents a discussion on the information systems and the data available and strategies of recommendation systems based on this scenario, integrating health surveillance, formative needs, georeference of health teams and professionals and epidemiological data to recommend content to health professionals all over the country.

For many decades society did need to monitor and assess the standard of living of the population. In the 1950s, the United Nations (UN) saw this need and proposed 12 areas that should be evaluated, the first of which is listed under "Health and Demography", which focuses on what is expressed as the level of a population's health. Decades have passed and great results have been gained from similar initiatives such as reducing mortality from infectious diseases and even eradicating some others. In the age of the digital society, needs have grown. Monitoring demands that once perished from data to become concrete now suffer from the opposite effect, the excess of data from everywhere. Healthcare systems around the world use many different information systems, collecting and generating hundreds of data at unimaginable speed. We are billions of people on the planet and most of us are connected to the virtual world, sharing information, experiences and events with some kind of cloud. In this information age, the ability to aggregate and process this data is a major factor in raising public health to a new level. The development of tools capable of analyzing a large volume of data in seconds and producing knowledge for targeted decision making can help in the fight against specific diseases, in the process of continuing education of professionals, in the formation of new professionals, in the elaboration of new policies. with the specific locoregional look, in the analysis of hidden trends in front of so much information faced in everyday life and other possibilities. The present work proposes an architecture capable of storing and manipulating seeking to standardize the variables in order to allow to correlate this large amount of data in a systematic way, providing to several services and researchers the possibility of consuming health, social, economic and educational data for the promotion of public health.

International audience

International audience

Social web permits users to acquire information from anonymous people around the world. This leads to a serious question about the trustworthiness of information and sources. During the last decade, numerous models were proposed to model social trust in the service of social web. Trust modeling follows two main axes, local trust (trust between pair of users), and global trust (user's reputation within the community). Subjective logic, is an extension of probabilistic logic that deals with the cases of lack of evidences. An elaborated local trust model based on subjective logic already exists. The aim of this work is to apply this model to the first time on a real data set. Then, we propose another global trust model based also on subjective logic. We apply both models on a real data set of a question answering social network that aims to assist people to find solutions to their technical problems in various domains. Our proposed global trust model ensures a better performance thanks to its precise interpretation of the context of trust, and its ability to satisfy new arrived users.

In Proceedings ICSL'90 (International conference on spoken language processing), Kobe (Japon), novembre 1990

In Proceedings EUROSPEECH 89 (European conference on speech communication and technology), Paris, septembre 1989