Educational Data Mining: An Application of Regressors in Predicting School Dropout

Nascimento, Rafaella Leandra Souza do; Neves, Ricardo Batista das; Neto, Manoel Alves de Almeida; Fagundes, Roberta

doi:10.1007/978-3-319-96133-0_19

Cited by 19 publications

(9 citation statements)

References 10 publications

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“…Several researchers have also recommended machine learning and related predictive modeling methods to study large SA/Abased data sets to help inform such algorithms and early warning systems (do Nascimento et al, 2018). , for example, utilized random forests in machine learning to predict student dropout among 165,715 Korean students.…”

Section: Second Tier Of the Pyramid: Early Warning And Nimble Responsementioning

confidence: 99%

School Attendance and Problematic School Absenteeism in Youth

2020

Frontiers Research Topics

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Frontiers is more than just an open-access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers Journal SeriesThe Frontiers Journal Series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers Journal Series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to QualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation.

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Section: Second Tier Of the Pyramid: Early Warning And Nimble Responsementioning

confidence: 99%

School Attendance and Problematic School Absenteeism in Youth

2020

Frontiers Research Topics

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“…Several researchers have also recommended machine learning and related predictive modeling methods to study large SA/A-based data sets to help inform such algorithms and early warning systems (do Nascimento et al, 2018). Chung and Lee (2019), for example, utilized random forests in machine learning to predict student dropout among 165,715 Korean students.…”

Section: A Multidimensional Multi-tiered System Of Supports Pyramidmentioning

confidence: 99%

Reconciling Contemporary Approaches to School Attendance and School Absenteeism: Toward Promotion and Nimble Response, Global Policy Review and Implementation, and Future Adaptability (Part 2)

et al. 2019

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As noted in Part 1 of this two-part review, school attendance is an important foundational competency for children and adolescents, and school absenteeism has been linked to myriad short- and long-term negative consequences, even into adulthood. Categorical and dimensional approaches for this population have been developed. This article (Part 2 of a two-part review) discusses compatibilities of categorical and dimensional approaches for school attendance and school absenteeism and how these approaches can inform one another. The article also poses a multidimensional multi-tiered system of supports pyramid model as a mechanism for reconciling these approaches, promoting school attendance (and/or prevention of school absenteeism), establishing early warning systems for nimble response to school attendance problems, assisting with global policy review and dissemination and implementation, and adapting to future changes in education and technology.

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“…Em relação a estimar variáveis a partir de indicadores educacionais utilizando de modelos de regressão, em do Nascimento et al (2018a) aplica-se técnicas de regressão linear e robusta com a finalidade de melhor explicar indicadores como a evasão e reprovação escolar no ensino fundamental. Ainda, em do Nascimento et al (2018b), estima-se a evasão escolar aplicando técnicas como a regressão quantílica não-paramétrica e a Support Vector Regression (SVR).…”

Section: Trabalhos Relacionadosunclassified

Estudo sobre Docentes do Ensino Básico através de Indicadores Educacionais e Modelos de Regressão

Nascimento¹,

Júnior²

2018

Workshops Do Congresso Brasileiro De Informática Na Educação

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This work uses educational databases provided by INEP and applies regression techniques with the purpose of estimating the indicator of teachers with higher education in the scenario of basic education. This application of Educational Data Mining is based on the phases of the CRISP-DM methodology. After correlational analyzes of the variables, linear and robust regression models were applied in order to compare the performance and provide a model that minimizes the prediction error. The models were evaluated by absolute mean error in addition to graphs and statistical tests. The results indicate that the robust regression obtained better results in the estimation of the variable listed in this study.Resumo. Este trabalho utiliza bases de dados educacionais fornecidas pelo INEP e aplica técnicas de regressão com o objetivo de estimar o indicador de docentes com curso superior no cenário da educação básica. Essa aplicação de Mineração de Dados Educacionais segue como base as fases da metodologia CRISP-DM. Após análises correlacionais das variáveis, modelos de regressões linear e robusta foram aplicados com a finalidade de comparar o desempenho e fornecer um modelo que minimize o erro de previsão. Os modelos foram avaliados pelo erro médio absoluto além de gráficos e testes estatísticos. Os resultados indicam que a regressão robusta obteve melhores resultados na estimação da variável elencada nesse estudo.

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Educational Data Mining: An Application of Regressors in Predicting School Dropout

Cited by 19 publications

References 10 publications

School Attendance and Problematic School Absenteeism in Youth

School Attendance and Problematic School Absenteeism in Youth

Reconciling Contemporary Approaches to School Attendance and School Absenteeism: Toward Promotion and Nimble Response, Global Policy Review and Implementation, and Future Adaptability (Part 2)

Estudo sobre Docentes do Ensino Básico através de Indicadores Educacionais e Modelos de Regressão

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