Technical and pedagogical feedback on the deployment of an ePortfolio. Models of the uses, analysis and perspectives

ISBN: 9782954014418International audienceThis paper asks how are being designed and expected modes of integration of students during and after their university studies in the specific context of development on private and public markets for applications such as e-digital portfolios. She also questioned the manner in which to deploy the strategies and institutional policies regarding the choice of digital interfaces for the enhancement of learning and using the integration of students and in particular the way is taken into account the research dimension a tool to select and deploy. To do this, it relies on a study conducted as part of e-inclusion project supported by the Office for Students professional insertion (BAIP) and funded by the University of Lorraine and the Regional Council of Lorraine. This study is based on monitoring of a panel of about 250 students and fifteen teachers experimenting "Lorfolio" in their regular educational setting. Lorfolio is a portfolio of digital skills remotely accessible, for all the assets of a territory, and being developed in Lorraine at the initiative of the Regional Council. We propose in particular to highlight the returns through the use of specific questions that are generated when it comes to decide on their wide deployment of an institution, consortium or territory. What models to use is based does? What actual uses generates does? How to use these models and are they related to the digital strategies of institutions? After recalling the context of the study, the actors and the methodology adopted, we will build on the first qualitative and quantitative analyzes performed to establish the first profiles of the perception of the tool from the point of view of students as teachers to measure the impact of such a deployment at institutional level

Quels impacts du déploiement d'un ePortfolio sur le cadre institutionnel et pédagogique ? Retour d'expérience

International audienceCette communication interroge la manière dont sont actuellement pensés et anticipés les modes d'insertion des étudiants pendant et à l'issue de leur parcours universitaire dans le contexte particulier du développement sur les marchés privés comme publics d'applications de type e-portfolios numériques. Elle interroge également les modalités selon lesquelles se déploient les stratégies et politiques des établissements en matière de choix d'interfaces numériques pour la valorisation des acquis et l'aide à l'insertion des étudiants et notamment la manière dont est prise en compte la dimension recherche pour choisir un outil et le déployer. Pour ce faire, elle s'appuie sur le cadre d'une étude menée dans le cadre du projet e-insertion soutenu par le BAIP (bureau d'aide à insertion professionnelle des étudiants) et subventionné conjointement par l'Université de Lorraine et le Conseil Régional de Lorraine. Cette étude repose sur le suivi d'un panel d'environ 250 étudiants et une quinzaine d'enseignants expérimentant " Lorfolio " dans leur cadre pédagogique habituel. Lorfolio est un portefeuille de compétences numérique accessible à distance, destiné à l'ensemble des actifs d'un territoire, et actuellement développé en région Lorraine à l'initiative du Conseil Régional. Nous proposons en particulier de mettre en évidence à travers les retours d'usage les questionnements particuliers qui sont générés dès qu'il s'agit de se prononcer sur leur déploiement à l'échelle d'un établissement, d'un consortium ou d'un territoire. Sur quels modèles d'usage repose-t-elle ? Quels usages effectifs génère-t-elle ? Comment ces modèles et usages s'articulent-ils aux stratégies numériques des établissements ? Après avoir rappelé le contexte de l'étude, les acteurs ainsi que la méthodologie adoptée, nous prendrons appui sur les premières analyses qualitatives et quantitatives effectuées pour dresser des premiers profils de la perception de l'outil tant du point de vue des étudiants que des enseignants afin de mesurer l'impact d'un tel déploiement au niveau institutionnel

Human Computer Collaboration to Improve Annotations in Semantic Wikis

International audienceSemantic wikis are very promising tools for producing structured and unstructured data. However, they suffer from a lack of user provided semantic annotations, resulting in a loss of efficiency, despite of their high potential. This paper focuses on an original way to encourage users to annotate semantically pages. We propose a system that suggests automatically computed annotations to users. Users thus only have to validate, complete, modify, refuse or ignore these suggested annotations. We assume that as the annotation task becomes easier, more users will provide annotations. The system we propose is based on collaborative filtering recommender systems, it does not exploit the content of the pages but the usage made on these pages by the users: annotations are deduced from the usage of the pages and the annotations previously provided. The resulting semantic wikis contain several kinds of annotations that are differentiated by their status: human provided annotations, computer provided annotations (suggested by the system), human-computed interactions (suggested by the system and validated by the users) and refused annotations (suggested by the system and refused by the user). Navigation and (semantic) search will thus be facilitated and more efficient

Considering temporal aspects in recommender systems: a survey

Under embargo until: 2023-07-04The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.acceptedVersio

A Low-Order Markov Model integrating Long-Distance Histories for Collaborative Recommender Systems

International audienceRecommender systems provide users with pertinent resources according their context and their profiles, by applying statistical and knowledge discovery techniques. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream, by considering long and short-distance resources in the history with a tractable model. The Skipping Based Recommender we propose uses Markov models inspired from the ones used in language modeling while integrating skipping techniques to handle noise during navigation. Weighting schemes are also used to alleviate the importance of distant resources. This recommender has also the characteristic to be anytime. It has been tested on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous resources to compute recommendations enhances the accuracy. Moreover, the skipping variant we propose provides a high accuracy while being less complex than state of the art variants

User Perceived Qualities and Acceptance of Recommender Systems:The Role of Diversity

Recommender systems have become important, as users are faced with an ever-increasing amount of information available on internet. Much of the research work on the topic has been focused on recommendation techniques, aiming at improving the accuracy of recommended items. Today, researchers use accuracy-metrics for evaluating goodness, when in fact these do not capture users' expectations and criteria for evaluating recommendation usefulness. We must ask ourselves whether a less accurate recommendation is necessarily a less valuable one for the user. To support this, we centre our investigations in this thesis on users, and explore their acceptance behaviours when using recommendations, and their perceived qualities. We present results in four areas. First, we study users' perceptions leading to the acceptance of recommendations and the possible long-term adoption of the system. We run two user studies using two online music recommenders relying on different recommendation techniques. Our results show that the perceived usefulness in terms of quality, and the perceived ease of use in terms of effort, are directly correlated with the users' acceptance of the recommendations. The results also show the necessity for low-involvement recommenders to be highly reactive, helping to take the users' search context into account. Secondly, we evaluate a behavioural recommender, where recommendations are made from implicitly expressed user preferences. We take profile sizes into account and compare such recommendations to an explicit search & browse interface. Our experiment reveals that users perceive the smaller effort required to use a behavioural recommender, but find the explicit solution to yield more diverse suggestions and gives them more control. Overall, users perceive both approaches as being satisfactory, providing the profile size is big enough. Thirdly, we analyse the impact on users' perceptions of a visual rendering. We designed an iconised representation of compound critiques, usually textual, and observed the differences in users' appreciation. Our results reveal that users prefer the visual interface, that it reduces their interaction efforts, and that users are attracted to apply the critiques more frequently in complex product domains, which have more product-features. In a fourth area, we examine the role of diversity of recommendations in users' acceptance. A first study shows that diversity is the dimension which most influences users' satisfaction. We also highlight that users have more confidence in their choice using an organised layout interface for the same perceived ease of use as with a list view, even though the organised layout creates longer interactions. For the first time in a study, we show that diversity correlates with the trust of users. In a second study, we use an eye-tracker to carry out an in-depth study of users' decision process. We show how the influence of a recommender increases throughout a user's purchase decision process until the decision is close to being taken. At this moment, we observed that users rely on the recommender to enhance their confidence in the purchase decision, and that they need diversity to prioritise the suggestions. To end our work, we propose a theoretical diversity-model for maximising users' overall satisfaction by balancing users' needs for recommendation accuracy and diversity throughout the decision process. In addition, we derive a set of design guidelines from all of the experimental results. They are elaborated around four primary axes: user effort, purchase intentions, complex systems and diversity