Anne Boyer

Anne Boyer
University of Lorraine | UdL · LORIA - Laboratoire Lorrain de Recherche en Informatique et Applications

HDR

About

193
Publications
16,621
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
818
Citations

Publications

Publications (193)
Conference Paper
Full-text available
The high failure rate is a major concern in distance online education. In recent years, Performance Prediction Systems (PPS) based on different analytical methods have been proposed to predict at-risk of failure learners. One of the main studied characteristics of these systems is its ability to provide accurate early predictions. However, these sy...
Chapter
Students’ effort is often considered to be a key element in the learning process. As such, it can be a relevant element to integrate in learning analytics tools, such as dashboards, intelligent tutoring systems, adaptive hypermedia systems, and recommendation systems. A prerequisite to do so is to measure and predict it from learning data, which po...
Chapter
Full-text available
Nowadays, the concept of education for all is gaining momentum thanks to the widespread use of e-learning systems around the world. The use of e-learning systems consists in providing learning content via the Internet to physically dispersed learners. The main challenge in this regard is the high fail rate particularly among k-12 learners who are o...
Chapter
Students who failed the final examination in the secondary school in France (known as baccalauréat or baccalaureate) can improve their scores by passing a remedial test. This test consists of two oral examinations in two subjects of the student’s choice. Students announce their choice on the day of the remedial test. Additionally, the secondary edu...
Preprint
Full-text available
Data mining is the task of discovering interesting, unexpected or valuable structures in large datasets and transforming them into an understandable structure for further use . Different approaches in the domain of data mining have been proposed, among which pattern mining is the most important one. Pattern mining mining involves extracting interes...
Conference Paper
Nowadays, Learning Record Stores (LRS) are increasingly used within digital learning systems to store learning experiences. Multiple LRS software have made their appearance in the market. These systems provide the same basic functional features including receiving, storing and retrieving learning records. Further, some of them may offer varying fea...
Chapter
Recommending students useful and effective learning paths is highly valuable to improve their learning experience. The evaluation of the effectiveness of this recommendation is a challenging task that can be performed online or offline. Online evaluation is highly popular but it relies on actual path recommendations to students, which may have dram...
Chapter
Effort is considered a key factor of students’ success and its influences on learning outcomes have been studied for decades. To study this relationship, researchers have been measuring it in several different ways. One traditional way to measure effort is to rely on indicators such as the time spent on a task. This solution is not entirely reliabl...
Preprint
Full-text available
The goals of Learning Analytics (LA) are manifold, among which helping students to understand their academic progress and improving their learning process, which are at the core of our work. To reach this goal, LA relies on educational data: students' traces of activities on VLE, or academic, socio-demographic information, information about teacher...
Chapter
Intelligent Tutoring Systems are now mature technologies that successfully help students to acquire new knowledge and competencies through various educational methods and in a personalized way. However, evaluating precisely what they recall at the end of the learning process remains a complex task. In this paper, we study if there are correlations...
Article
Full-text available
Purpose The purpose of this paper is to present the METAL project, a French open learning analytics (LA) project for secondary school, that aims at improving the quality of teaching. The originality of METAL is that it relies on research through exploratory activities and focuses on all the aspects of a learning analytics environment. Design/meth...
Chapter
When people use recommender systems, they generally expect coherent lists of items. Depending on the application domain, it can be a playlist of songs they are likely to enjoy in their favorite online music service, a set of educational resources to acquire new competencies through an intelligent tutoring system, or a sequence of exhibits to discov...
Preprint
Full-text available
This paper presents the METAL project, an ongoing French open Learning Analytics (LA) project for secondary school, that aims at improving the quality of the learning process. The originality of METAL is that it relies on research through exploratory activities and focuses on all the aspects of a Learning Analytics implementation. This large-scale...
Article
Sequential pattern mining has been the focus of many works, but still faces a tough challenge in the mining of large databases for both efficiency and apprehensibility of its resulting set. To overcome these issues, the most promising direction taken by the literature relies on the use of constraints, including the well-known closedness constraint....
Conference Paper
Full-text available
Students' effort is often considered a key factor for students' success. It has several related definitions, none of which is widely adopted. In this paper, we define students' effort as the experienced cognitive load, which is the total amount of cognitive resources used during the execution of a given task. We propose an effort model to quantify...
Article
Many recommenders compute predictions by inferring the users' preferences. However, in some cases, such as in e-education, the recommendations of pedagogical resources should rather be based on users' memory. In order to estimate in real time and with low involvement what has been recalled by users, we designed a user study to highlight the link be...
Article
Events prediction in a sequence of events is a challenging task that can be approached with data mining. In this paper, we focus on the specific case of early prediction of distant events. We aim at mining episode rules with a consequent temporally distant from the antecedent and an antecedent as small as possible both in number of events and in oc...
Conference Paper
Full-text available
In this theses, we formally prove that the classification rules formed on the basis of contrast patterns are guaranteed to be of a high quality. We propose to use the new ‘Sets of Contrasting Rules’ pattern for the identification of differences between the classes of the dataset. Being essentially a contrast pattern formed of several classification...
Conference Paper
Data clustering is an important topic in data science in general, but also in user modeling and recommendation systems. Some clustering algorithms like K-means require the adjustment of many parameters, and force the clustering without considering the clusterability of the dataset. Others, like DBSCAN, are adjusted to a fixed density threshold, so...
Conference Paper
Context-aware recommendation became a major topic of interest within the recommender systems community as the context is crucial to provide the right items at the right moment. Many studies aim at developing complex models to include contextual factors in the recommendation process. Despite a real improvement on the recommendations quality, such co...
Conference Paper
Mining closed contiguous sequential patterns has been addressed in the literature only recently, through the CCSpan algorithm. CCSpan mines a set of patterns that contains the same information than traditional sets of closed sequential patterns, while being more compact due to the contiguity. Although CCSpan outperforms closed sequential pattern mi...
Article
Full-text available
Matrix factorization has proven to be one of the most accurate recommendation approaches. However, it faces one major shortcoming: the latent features that result from the factorization are not directly interpretable. Providing interpretation for these features is important not only to help explain the recommendations presented to users, but also t...
Article
De nombreuses études ont démontré que la prise en compte du contexte améliore la qualité des systèmes de recommandation. Cependant, les méthodes traditionnelles permettent d'inférer le contexte à l'aide de données personnelles (localisation, date, âge, etc.). Dans ce papier, nous proposons de détecter automatiquement les changements de contexte, sa...
Conference Paper
The collaborative filtering (CF) approach in recommender systems assumes that users' preferences are consistent among users. Although accurate, this approach fails on some users. We presume that some of these users belong to a small community of users who have unusual preferences, such users are not compliant with the CF underlying assumption. They...
Conference Paper
Recommender systems usually rely on users' preferences. Nevertheless, there are many situations (e-learning, e-health) where recommendations should rather be based on their memory. So as to infer in real time and with low involvement what has been memorized by users, we propose in this paper to establish a link between gaze features and visual memo...
Conference Paper
The main goal of recommender systems is to help users to filter all the information available by suggesting items they may like without they had to find them by themselves. Although the rating prediction is a pretty well controlled topic, being able to make a recommendation at the right moment still remain a challenging task. To this end, most rese...
Article
Being able to automatically and quickly understand the user context during a session is a main issue for recommender systems. As a first step toward achieving that goal, we propose a model that observes in real time the diversity brought by each item relatively to a short sequence of consultations, corresponding to the recent user history. Our mode...
Conference Paper
The social approach in recommender systems relies on the hypothesis that preferences are coherent between users. To recommend a user u some resources, this approach exploits the preferences of other users who have preferences similar to those of u. Although this approach has shown to produce on average high quality recommendations, which makes it t...
Article
Full-text available
Episode rules are event patterns mined from a single event sequence. They are mainly used to predict the occurrence of events (the consequent of the rule), once the antecedent has occurred. The occurrence of the consequent of a rule may however be disturbed by the occurrence of another event in the sequence (that does not belong to the antecedent)....
Conference Paper
Recommender system provides relevant items to users from huge catalogue. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Collaborative filtering uses only the user-ratings data to make predictions, while content-based filtering relies on semantic information of items for r...
Article
Full-text available
Unlike other works, this paper aims at searching a connection between two most popular approaches in recommender systems domain: Neighborhood-based (NB) and Matrix Factorization-based (MF). Provided analysis helps better understand advantages and disadvantages of each approach as well as their compatibility. While NB relies on the ratings of simila...
Article
Full-text available
Trust is an imperative issue in any human society. It is built up with the survey of recurrent interactions between fellows. By consequence, trust is sensible to the time, which we call the temporal factor is trust relationship. During the last decade, the arise of social web resulted a serious need to a trust model for this virtual society. Many m...
Conference Paper
Full-text available
Searching and recommendation are basic functions that effectively assist learners to approach their favorite learning resources. Several searching and recommendation techniques in the Information Retrieval (IR) domain have been proposed to apply in the Technology Enhanced Learning (TEL) domain. However, few of them pay attention on particular prope...
Conference Paper
Since the emergence of learning analytics in North America, researchers and practitioners have worked to develop an international community. The organization of events such as SoLAR Flares and LASI Locals, as well as the move of LAK in 2013 from North America to Europe, has supported this aim. There are now thriving learning analytics groups in Nor...
Article
The social approach in recommender systems relies on the hypothesis that users' preferences are coherent between users. To recommend a user some items, it uses the preferences of other users, who have preferences similar to those of this user. Although this approach has shown to produce on average high quality recommendations, which makes it the mo...
Article
Les Learning Analytics – ou analyse de l’apprentissage – constituent une discipline émergente à la confluence de l’informatique, des sciences de l’éducation et des mathématiques. Leur objet d’étude est la collecte, l’analyse et l’utilisation intelligentes de données produites par l’apprenant. Si les Learning Analytics puisent leurs techniques dans...
Conference Paper
In E-learning context, we can recommend pedagogical resources to help learners. In this context, the recommender proposes the nearest resource(s) in term of similarity, but the scarcity of resources may affects seriously the quality of predictions. To make accurate predictions we begin in determining the scarce resources to be taken into account in...
Conference Paper
Full-text available
This paper focuses on event prediction in an event sequence, particularly on distant event prediction. We aim at mining episode rules with a consequent temporally distant from the antecedent and with a minimal antecedent. To reach this goal, we propose an algorithm that determines the consequent of an episode rule at an early stage in the mining pr...
Conference Paper
Full-text available
This paper focuses on event prediction in an event sequence, where we aim at predicting distant events. We propose an algorithm that mines episode rules, which are minimal and have a consequent temporally distant from the antecedent. As traditional algorithms are not able to mine directly rules with such characteristics, we propose an original way...
Conference Paper
Full-text available
Technologies supporting online education have been abundantly developed recent years. Many repositories of digital learning resources have been set up and many recommendation approaches have been proposed to facilitate the consummation of learning resources. In this paper, we present an approach that combines three recommendation technologies: cont...
Article
Full-text available
Let's imagine a system that can recommend the kind of music (among other application domains) you like to listen when you are at work, without having to know your location, IP address or even to ask your current mood. In this paper, we bring this dream closer by proposing a model that can automatically understand the user's current context. This mo...
Conference Paper
Full-text available
Recommender system provides relevant items to users from huge catalogue. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Collab- orative filtering uses only the user-ratings data to make predictions, while content-based filtering relies on semantic information of items for rec...
Article
Full-text available
Today, trust modelling is a serious issue on the social web. Social web allows anonymous users to exchange information without even knowing each other beforehand. The aim of a trust model is to rerank acquired information according to their reliability, and to the trustworthiness of their authors. During the last decade, trust models were proposed...
Conference Paper
Full-text available
Social web permits users to acquire information from anonymous people around the world. This leads to a serious question about the trustworthiness of the information and the sources. During the last decade, numerous models were proposed to adapt social trust to social web. These models aim to assist the user in becoming able to state his opinion ab...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
Recommender system provides relevant items to users from huge catalogue. Collaborative filtering and content- based filtering are the most widely used techniques in person- alized recommender systems. Collaborative filtering uses only the user-ratings data to make predictions, while content-based filtering relies on semantic information of items for re...
Chapter
The development of Internet and the success of Web 2.0 applications engendered the emergence of virtual communities. Analyzing information flows and discovering leaders through these communities becomes thus a major challenge in different application areas. In this work, we present an algorithm that aims at detecting leaders through the use of impl...
Article
Predictive web usage modeling has undergone an intense period of investigation until the late 90's. However, two features of web browsing have rarely been taken into account: the presence of noise and parallel browsing. In this paper, we propose a new model, the SBR model (Skipping-Based Recommender) which uses a technique called skipping, and is a...
Conference Paper
Full-text available
Recommender systems (RS) are designed to assist users by recommending them items they should appreciate. User based RS exploits users behavior to generate recommendations. Users act in accordance with different modes when using RS, so RS’s performance fluctuates across users, depending on their act mode. Act here includes quantitative and qualitati...
Conference Paper
Full-text available
Personalized recommender systems provide relevant items to users from huge catalogue. Collaborative filtering (CF) and content-based (CB) filter- ing are the most widely used techniques in personalized recommender systems. CF uses only the user-rating data to make predictions, while CB filtering relies on semantic information of items for recomm...
Conference Paper
Full-text available
Recommender systems (RS) are designed to assist users by recommending them items they should appreciate. User based RS exploit users behavior to generate recommendations. As a matter of fact, RS performance fluctuates across users. We are interested in analyzing the characteristics and behavior that make a user receives more accurate/inaccurate rec...
Conference Paper
Full-text available
Recommender systems (RS) aim to predict items that users would appreciate, over a list of items. In evaluation of recommender systems, two issues can be defined: accuracy of prediction which implies the satisfaction of the user, coverage which implies the percentage of satisfied users. Collaborative filtering (CF) is the master approach in this dom...
Article
Personalization services, which have emerged with Web 2.0, rely on the exploitation of user models. The more significant the amount of available information about users, the better the quality of the model and of the service. However, many services suffer from the data sparsity problem. In this paper, we choose to ease this problem by performing a...