EvolveDTree: Analyzing Student Dropout in Universities

Santos, G. A. S.; Belloze, Kele Teixeira; Tarrataca, Luís; Haddad, Diego B.; Bordignon, Alex; Brandão, Diego

doi:10.1109/iwssip48289.2020.9145203

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…Refs. [21][22][23][24][25][26][27][28] predicted the students' performance and whether or not the student will drop out. Researchers in Ref.…”

Section: Predicting Student Dropout Authors Inmentioning

confidence: 99%

“…Researchers in Ref. [21] discovered the patterns that contribute to prevent at-risk students from drop out. is study aimed to detect whether or not the student will be evaded by using the decision tree (DT) for classification and genetic algorithm (GA) for feature selection.…”

Section: Predicting Student Dropout Authors Inmentioning

confidence: 99%

“…Recall (63.4%) [59] Improved ID3 Accuracy (74%) (CGPA) and assessment grades are university features that are frequently used as indicators to predict student performance. A group of studies [21,23,24] found that CGPA is the most influential attribute to identify at-risk students who may drop out. Furthermore, assessment grades are considered in several studies (i.e., [29,32,34,42] and [44]) as influential factors to predict student achievement at the end of the course.…”

Section: E Influential Factors For Predicting Student's Performancementioning

confidence: 99%

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Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Alwarthan

Aslam

Khan

2022

Applied Computational Intelligence and Soft Computing

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Recently, educational institutions faced many challenges. One of these challenges is the huge amount of educational data that can be used to discover new insights that have a significant contribution to students, teachers, and administrators. Nowadays, researchers from numerous domains are very interested in increasing the quality of learning in educational institutions in order to improve student success and learning outcomes. Several studies have been made to predict student achievement at various levels. Most of the previous studies were focused on predicting student performance at graduation time or at the level of a specific course. The main objective of this paper is to highlight the recently published studies for predicting student academic performance in higher education. Moreover, this study aims to identify the most commonly used techniques for predicting the student's academic level. In addition, this study summarized the highest influential features used for predicting the student academic performance where identifying the most influential factors on student’s performance level will help the student as well as the policymakers and will give detailed insights into the problem. Finally, the results showed that the RF and ensemble model were the most accurate models as they outperformed other models in many previous studies. In addition, researchers in previous studies did not agree on whether the admission requirements have a strong relationship with students' achievement or not, indicating the need to address this issue. Moreover, it has been noticed that there are few studies which predict the student academic performance using students’ data in arts and humanities major.

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“…Refs. [21][22][23][24][25][26][27][28] predicted the students' performance and whether or not the student will drop out. Researchers in Ref.…”

Section: Predicting Student Dropout Authors Inmentioning

confidence: 99%

Section: Predicting Student Dropout Authors Inmentioning

confidence: 99%

Section: E Influential Factors For Predicting Student's Performancementioning

confidence: 99%

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Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Alwarthan

Aslam

Khan

2022

Applied Computational Intelligence and Soft Computing

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“…A cada gerac ¸ão são realizadas recombinac ¸ões dos indivíduos mais aptos por meio das operac ¸ões de mutac ¸ão e cruzamento, formando uma nova populac ¸ão de indivíduos para a próxima gerac ¸ão. Esse procedimento é feito de forma iterativa até que um critério de parada seja alcanc ¸ado, por exemplo atingir um determinado número de gerac ¸ões ou atingir determinado valor na func ¸ão objetivo [Tan et al, 2008;Santos et al, 2020].…”

Section: Fundamentac ¸ãO Teóricaunclassified

“…Muitos trabalhos utilizam métodos de filtro com critérios estatísticos de ranqueamento devido a facilidade do seu uso [Febro, 2019]. Outros utilizam abordagem wrapper com o uso de algoritmo genético (AG) para selecionar o melhor conjunto de atributos que definem o cenário escolar e acadêmico [Farissi et al, 2020;Santos et al, 2020], com maior custo computacional. Outros ainda aplicam o próprio classificador em uma abordagem denominada de embutida [Gitinabard et al, 2018].…”

Section: Introduc ¸ãOunclassified

Um algoritmo genético com função de aptidão flexível para seleção de atributos em dados educacionais

Albuquerque

Brandão

Coutinho

2021

Anais Do XXXVI Simpósio Brasileiro De Banco De Dados (SBBD 2021)

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Devido ao volume crescente e à disponibilidade cada vez maior de dados educacionais, as técnicas de mineração de dados têm sido frequentemente aplicadas para auxiliar na compreensão do desempenho escolar do aluno. No entanto, muitos desses dados podem ser esparsos, redundantes, irrelevantes e com ruído, o que pode prejudicar a qualidade e o desempenho computacional dos modelos preditivos. Uma maneira de minimizar esses problemas consiste em selecionar atributos no processo de modelagem por meio de técnicas de Seleção de Atributos (SA). Este artigo propõe uma abordagem de SA com algoritmo genético adaptada ao contexto educacional. Os resultados indicam que a proposta melhora o desempenho da classificação e permite que especialistas em educação tenham uma maior flexibilidade no processo de seleção dos atributos de acordo com suas necessidades e realidades.

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Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature

Fahd

Venkatraman²,

Miah

et al. 2021

Educ Inf Technol

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Recently, the field of machine learning (ML) has evolved and finds its application in higher education (HE) for various data analysis. Studies have shown that such an emerging field in educational technology provides meaningful insights into several dimensions of educational quality. An in-depth analysis of the application of ML could have a positive impact on the HE sector. However, there is a scarcity of a systematic review of HE literature to gain from the overarching trends and patterns discovered using ML. This paper conducts a systematic review and meta-analyses of research studies that have reported on the application of ML in HE. The differentiating factors of this study are primarily vested in the meta-analyses including a specific focus on student academic performance, atrisk, and attrition in HE. Our detailed investigation adopts an evidence-based framework called PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for reporting the findings of our systematic review and meta-analyses of literature on the use of ML models, algorithms, evaluation metrics, and other criteria including demographics for assessing student academic performance, at-risk and attrition in HE. After undergoing the PRISMA steps such as selection criteria and filtering, we arrive at a narrowed down dataset of 89 relevant studies published from 2010 to 2020 for an in-depth analysis. The results not only show the outcomes of the quantitative analysis of the application of ML types, models, evaluation metrics, and other related demographics but also provide quality insights of publication patterns and future trends towards predicting and monitoring student academic progress in HE.

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EvolveDTree: Analyzing Student Dropout in Universities

Cited by 10 publications

References 14 publications

Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Um algoritmo genético com função de aptidão flexível para seleção de atributos em dados educacionais

Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature

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