Prediction of academic dropout in a higher education institution using data mining

Este estudo propõe dois modelos preditivos de classificação que permitem identificar, logo no final do 1º e do 2º semestres escolares, os estudantes de licenciatura de uma instituição de ensino superior mais propensos ao abandono académico. A metodologia proposta, que combina 3 algoritmos populares de data mining, como são as random forest, as máquinas de vetores de suporte e as redes neuronais artificiais, para além de contribuir para a assertividade da previsão, permite identificar por ordem de relevância os principais fatores que prenunciam o abandono académico. Os resultados empíricos demonstram ser possível reduzir para cerca de 1/4 as 4 dezenas de potenciais preditores do abandono, e mostram serem essencialmente dois, do contexto curricular do estudante, a explicarem essa propensão. Esse conhecimento revela-se de importância primordial para que os agentes de gestão possam adotar as medidas e decisões estratégicas mais propícias à diminuição dos índices de evasão discente.This study proposes two predictive models of classification that allow to identify, at the end of the 1st and 2nd semesters, the undergraduate students of a higher education institution more prone to academic dropout. The proposed methodology, which combines 3 popular data mining algorithms, such as random forest, support vector machines and artificial neural networks, in addition to contributing to predictive performance, allows to identify the main factors behind academic dropout. The empirical results show that it is possible to reduce to about 1/4 the 4 tens potential predictors of dropout, and show that there are essentially two predictors, concerning student’s curriculum context, that explain this propensity. This knowledge is useful for decision-makers to adopt the most appropriate strategic measures and decisions in order to reduce student dropout rates.Este trabalho foi suportado pela Fundação para a Ciência e Tecnologia (FCT) através do Projeto UID/EEA/04131/2019. Agradece-se igualmente ao IPB, e em particular ao seu pró-presidente para os Sistemas de Informação, Prof. Doutor Albano Alves, pela disponibilização dos dados analisados no presente estudo.info:eu-repo/semantics/publishedVersio

Repercussion of telemonitoring as a self-care strategy for diabetes mellitus people / Repercussão do telemonitoramento como estratégia para o autocuidado às pessoas com diabetes mellitus

RESUMO Objetivo: analisar as produções científicas sobre o telemonitoramento e suas repercussões no acompanhamento do autocuidado de pessoas com Diabetes Mellitus tipo 2 (DM2). Métodos: trata-se de revisão integrativa da literatura, realizada nas bases de dados BVS (LILACS, BDENF, MEDLINE) e PUBMED nos meses de junho a julho de 2018, com recorte temporal de cinco anos. Resultados: a amostra é constituída de 10 artigos sobre a temática e, a partir de associações temáticas, foi nomeada em duas categorias: repercussão da estratégia telefônica para o autocuidado e estratégia telefônica: controle e eficácia. Conclusão: o uso do telemonitoramento no acompanhamento de pessoas com DM2 teve boa repercussão e serviu como apoio, educação em saúde e monitoramento dos níveis glicêmicos. Dessa forma, houve melhorias no comportamento de saúde e satisfação com o serviço recebido e, com isso, demonstrou eficácia para o autocuidado

SMART-QUAL: a dashboard for quality measurement in higher education institutions

Purpose – The paper aims to define a dashboard of indicators to assess the quality performance of higher education institutions (HEI). The instrument is termed SMART-QUAL. Design/methodology/approach –Two sources were used in order to explore potential indicators. In the first step, information disclosed in official websites or institutional documentation of 36 selected HEIs was analyzed. This first step also included in depth structured high managers’ interviews. A total of 223 indicators emerged. In a second step, recent specialized literature was revised searching for indicators, capturing additional 302 indicators. Findings – Each one of the 525 total indicators was classified according to some attributes and distributed into 94 intermediate groups. These groups feed a debugging, prioritization and selection process, which ended up in the SMART-QUAL instrument: a set of 56 key performance indicators, which are grouped in 15 standards, and, in turn, classified into the 3 HEI missions. A basic model and an extended model are also proposed. Originality/value – The paper provides a useful measure of quality performance of HEIs, showing a holistic view to monitor HEI quality from three fundamental missions. This instrument might assist HEI managers for both assessing and benchmarking purposes. The paper ends with recommendations for university managers and public administration authorities

Modeling partial customer churn: on the value of first product-category purchase sequences

A

Analysis of Renewable Energy Policies through Decision Trees

This paper presents an alternative way of making predictions on the effectiveness and efficacy of Renewable Energy (RE) policies using Decision Trees (DT). As a data-driven process for decision-making, the analysis uses the Renewable Energy (RE) target achievement, predicting whether or not a RE target will likely be achieved (efficacy) and to what degree (effectiveness), depending on the different criteria, including geographical context, characterizing concerns, and policy characteristics. The results suggest different criteria that could help policymakers in designing policies with a higher propensity to achieve the desired goal. Using this tool, the policy decision-makers can better test/predict whether the target will be achieved and to what degree. The novelty in the present paper is the application of Machine Learning methods (through the Decision Trees) for energy policy analysis. Machine learning methodologies present an alternative way to pilot RE policies before spending lots of time, money, and other resources. We also find that using Machine Learning techniques underscores the importance of data availability. A general summary for policymakers has been included

Forecasting bivalve landings with multiple regression and data mining techniques: The case of the Portuguese Artisanal Dredge Fleet

This paper develops a decision support tool that can help fishery authorities to forecast bivalve landings for the dredge fleet accounting for several contextual conditions. These include weather conditions, phytotoxins episodes, stock-biomass indicators per species and tourism levels. Vessel characteristics and fishing effort are also taken into account for the estimation of landings. The relationship between these factors and monthly quantities landed per vessel is explored using multiple linear regression models and data mining techniques (random forests, support vector machines and neural networks). The models are specified for different regions in the Portugal mainland (Northwest, Southwest and South) using six years of data 2010-2015). Results showed that the impact of the contextual factors varies between regions and also depends on the vessels target species. The data mining techniques, namely the random forests, proved to be a robust decision support tool in this context, outperforming the predictive performance of the most popular technique used in this context, i.e. linear regression.Foundation for Science and Technology (FCT, Portugal) [SFRH/BPD/99570/2014]ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE Programme [POCI-01-0145-FEDER-006961]National Funds through the FCT Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) [UID/EEA/50014/2013]project MONTEREALMAR ProgramEuropean fund for Fisheries and Maritime Affairs (EFFM)Portuguese Governmentinfo:eu-repo/semantics/publishedVersio