58 research outputs found
Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations. A pedagogical and technical perspective
Learning personalization has proven its effectiveness in enhancing learner
performance. Therefore, modern digital learning platforms have been
increasingly depending on recommendation systems to offer learners personalized
suggestions of learning materials. Learners can utilize those recommendations
to acquire certain skills for the labor market or for their formal education.
Personalization can be based on several factors, such as personal preference,
social connections or learning context. In an educational environment, the
learning context plays an important role in generating sound recommendations,
which not only fulfill the preferences of the learner, but also correspond to
the pedagogical goals of the learning process. This is because a learning
context describes the actual situation of the learner at the moment of
requesting a learning recommendation. It provides information about the learner
current state of knowledge, goal orientation, motivation, needs, available
time, and other factors that reflect their status and may influence how
learning recommendations are perceived and utilized. Context aware recommender
systems have the potential to reflect the logic that a learning expert may
follow in recommending materials to students with respect to their status and
needs. In this paper, we review the state-of-the-art approaches for defining a
user learning-context. We provide an overview of the definitions available, as
well as the different factors that are considered when defining a context.
Moreover, we further investigate the links between those factors and their
pedagogical foundations in learning theories. We aim to provide a comprehensive
understanding of contextualized learning from both pedagogical and technical
points of view. By combining those two viewpoints, we aim to bridge a gap
between both domains, in terms of contextualizing learning recommendations
Ubiquitous Computing for Mobile Environments
The increasing role and importance of ubiquitous computing and mobile environments in our daily lives implies the need for new solutions. The characteristics of agents and multi-agent systems make them very appropriate for constructing ubiquitous and mobile systems. This chapter presents some of the advances in practical and theoretical applications of multi-agent systems in the fields of ubiquitous computing and mobile environments carried out by several AgentCities.ES research groups
Actas da 10ª Conferência sobre Redes de Computadores
Universidade do MinhoCCTCCentro AlgoritmiCisco SystemsIEEE Portugal Sectio
Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review
A health recommender system (HRS) provides a user with personalized medical information based on the user’s health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management
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