Competency-based engineering curriculum modeling: a systematic mapping of literature

La aprobación de los Estándares de Segunda Generación para la Acreditación de Carreras de Ingeniería, por parte del Consejo Federal de Decanos de Ingeniería de Argentina, instaura en las universidades la formación por competencias con fuerte impacto en los currículos. Este proceso es de complejidad tal que es conveniente que los interesados sean asistidos con productos de software. En este trabajo se presenta un mapeo sistemático de la literatura para obtener las investigaciones que detallen la aplicación de modelos legibles por máquinas que soporten el desarrollo de estos sistemas. Dicho mapeo revela la necesidad de continuar investigando y construir un modelo formal que satisfaga los requerimientos de sistemas de generación de currículos para la formación por competencias.The approval of the Second Generation Standards for the Accreditation of Engineering Careers, by the Federal Council of Engineering Deans of Argentina, establishes competency-based training in universities with a strong impact on curricula. This process is of such complexity that it is convenient for interested parties to be assisted with software products. This paper presents a systematic mapping of the literature to obtain research detailing the application of machine-readable models that support the development of these systems. This mapping reveals the need to continue researching and building a formal model that satisfies the requirements of curriculum generation systems for competency-based training.Sociedad Argentina de Informática e Investigación Operativ

Sistema de recomendação na área do desporto

Este projeto está inserido num ramo de inteligência artificial chamado Machine Learning e este baseia-se na ideia de que os sistemas possam aprender com informação, identificar padrões e por sua vez tomar decisões com o mínimo de intervenção humana. Machine Learning é utilizado no dia-a-dia em recomendações online de produtos, deteção de fraudes, anúncios em tempo real, reconhecimento de voz e texto, entre outros. A presente dissertação tem como objetivo documentar todo o processo de implementação de um sistema de recomendação de anúncios em tempo real na área do desporto. O sistema de recomendação (baseado em conteúdo) com base no perfil de cada utilizador associa os anúncios desportivos que correspondem com a sua procura ou oferta e envia-lhe uma notificação. Os utilizadores têm acesso às características dos anúncios, mas só poderão ver o proprietário do anúncio e entrar em contacto com ele se usufruírem de uma conta premium. Este sistema permite aos utilizadores criarem e visualizarem anúncios desportivos em várias modalidades, assim estes poderão analisar as melhores ofertas ou procuras atualmente no mercado. O sistema de recomendação é composto por uma solução web desenvolvida em ASP.NET MVC e uma solução móvel desenvolvida em React Native e visa promover uma nova abordagem do processo de captação de jogadores e treinadores.This project is embedded in an artificial intelligence branch called Machine Learning and this is based on the idea that systems can learn from information, identify patterns and make own decisions with minimal human intervention. Machine Learning is used daily in online product recommendations, fraud detection, realtime announcements, voice and text recognition, among others. The present dissertation aims to document the entire process of implementing a realtime announcements recommendation system in sport area. The recommendation system (content-based) based on the profile of each user associates the sports announcements that match with his search or offer and sends him a notification. Users have access to the features of the announcements but will only be able to see the owner of the announcement and get in touch with him if they have a premium account. This system allows users to create and view sports announcements of different modalities, so they can analyze the best deals or searches currently on the market. The recommendation system consists of a web solution developed in ASP.NET MVC and a mobile solution developed in React Native and aims to promote a new approach to the process of capturing players and coaches

Recommender systems for human resources task assignment

In Portugal, the organisations responsible for the internal control of the State's financial administration need to progressively optimise their human resources in order to maximise their effectiveness. Part of this important responsibility relates to competence management and the assignment of their most suitable human resources to the tasks that insure their mission accomplishment. Such endeavour can benefit from a central concept of the Computer Supported Collaborative Work (CSCW) field: the application of computer technology to support group work. This paper outlines a recommender system, the 2HRT that aims to facilitate a more proficient human resources' task assignment, helping the human resources department to respond more efficiently to the demands for personnel of other departments. This research uses a Delphi study, with semi-structured interviews to collect the views of inspection agents in Portugal. The proposed recommender system incorporates the collaborative filtering and content-based recommendation techniques and the case-based reasoning approach