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Retention is one of the situations that can occur throughout the student’s academic life, which can be seen as an inefficiency of the brazilian educational system. Retention, in addition to causing waste of money in public higher education institutions, also fails to deliver qualified professionals to the labor market. In Brazilian higher education institutions, the average retention rate of students who entered in 2010 was 20% at the end of the fifth year of the undergraduate courses. In this context, the purpose of this article is to describe the adoption of data mining to analyze retention in undergraduate courses at Escola Politécnica de Pernambuco (POLI) to improve the understanding of the scenario and enable the definition and monitoring of actions in an attempt to reduce the retention rate and increase the number of graduate students. Carrying out actions to avoid retention, it is expected a more efficient use of the resources of brazilian public institutions of higher education and an increase in the number of qualified professionals to enter the labor market. A retenção ou permanência prolongada é uma das situações que pode ocorrer ao longo da vida acadêmica do aluno, podendo ser vista como ineficiência do sistema escolar. A retenção, além de causar desperdício de dinheiro nas instituições de ensino superior públicas, também deixa de entregar para o mercado de trabalho mão de obra qualificada. Nas instituições de ensino superior brasileiras, a taxa média de permanência era 20% ao final do quinto ano da graduação de ingressantes no ano de 2010. Diante desse contexto, o trabalho proposto descreve o uso de mineração de dados para analisar a retenção em Cursos de Graduação na Escola Politécnica de Pernambuco (POLI) para melhorar a compreensão do cenário e possibilitar a definição e acompanhamento de ações na tentativa de diminuir a taxa de retenção e aumentar a quantidade de concluintes. A partir da realização de ações no combate à retenção, espera-se o uso mais eficiente dos recursos das instituições públicas de ensino de educação superior no Brasil e um aumento no quantitativo de profissionais aptos a ingressarem no mercado de trabalho.

Honey bees are key insect pollinators, providing important economic and ecological value for human beings and ecosystems. This has triggered the development of several monitoring methods for assessing the temporal development of colony size, food storage, brood and pathogens. Nonetheless, most of these methods are based on visual assessments that are observer-dependent and prone to bias. Furthermore, the impact on colony development (invasiveness), as well as accuracy, were rarely considered when implementing new methods. In this study, we present and test a novel accurate and observer-independent method for honey bee colony assessment, capable of being fully standardized. Honey bee colony size is quantified by assessing the weight of adult bees, while brood and provision are assessed by taking photos and conducting image analysis of the combs with the image analysis software Deepbee®. The invasiveness and accuracy of the method were investigated using field data from two experimental apiaries in Portugal, comparing results from test and control colonies. At the end of each field experiment, most of the tested colonies had the same colony size, brood levels and honey production as the control colonies. Nonetheless, continuous weight data indicated some disturbance in tested colonies in the first year of monitoring. The overall accuracy of the image analysis software was improved by training, indicating that it is possible to adapt the software to local conditions. We conclude that the use of this fully quantitative method offers a more accurate alternative to classic visual colony assessments, with negligible impact on colony development. Honey bees are key insect pollinators, providing important economic and ecological valuefor human beings and ecosystems. This has triggered the development of several monitoringmethods for assessing the temporal development of colony size, food storage, broodand pathogens. Nonetheless, most of these methods are based on visual assessments thatare observer-dependent and prone to bias. Furthermore, the impact on colony development(invasiveness), as well as accuracy, were rarely considered when implementing new methods.In this study, we present and test a novel accurate and observer-independent method forhoney bee colony assessment, capable of being fully standardized. Honey bee colony size isquantified by assessing the weight of adult bees, while brood and provision are assessed bytaking photos and conducting image analysis of the combs with the image analysis softwareDeepbeeVR . The invasiveness and accuracy of the method were investigated using field datafrom two experimental apiaries in Portugal, comparing results from test and control colonies.At the end of each field experiment, most of the tested colonies had the same colony size,brood levels and honey production as the control colonies. Nonetheless, continuous weightdata indicated some disturbance in tested colonies in the first year of monitoring. The overallaccuracy of the image analysis software was improved by training, indicating that it ispossible to adapt the software to local conditions. We conclude that the use of this fullyquantitative method offers a more accurate alternative to classic visual colony assessments,with negligible impact on colony development.