On Improving Student Performance Prediction in Education Systems using Enhanced Data Mining Techniques

Karthikeyan, K.; Kavipriya, P.

doi:10.23956/ijarcsse/sv7i5/0348

Cited by 13 publications

(8 citation statements)

References 2 publications

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“…A PDA é um tema relevante que desperta interesse de pesquisadores e profissionais da educação, sendo importante considerar o papel do aluno na aprendizagem. A PDA possibilita alertar o aluno sobre sua jornada educacional e oferecer oportunidades de melhoria durante sua formação (Alamri;Alharbi, 2021;Karthikeyan;Kavipriya, 2017). É possível utilizar técnicas de mineração de dados educacionais e análise do aprendizado dos alunos para aprimorar a oferta de educação por meio de abordagens estatísticas e de AM (Al-Shabandar et al, 2017).…”

Section: Prever O Desempenho Dos Alunos -Pdaunclassified

“…O aprendizado supervisionado é uma técnica que permite a análise de dados para estimar os parâmetros do modelo. Ele utiliza técnicas de regressão básica e classificação, e pode ser aplicado em projetos de diversas áreas, como logística e reconhecimento de imagens (Karthikeyan;Kavipriya, 2017). Organizações como Google, OpenAI, Meta, FedEx, Amazon, Mercado Livre e Walmart, têm utilizado essa técnica em seus projetos para melhorar suas operações.…”

Section: Algoritmos Tipos De Aprendizado E Ferramentasunclassified

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Prever e melhorar o desempenho dos alunos com o uso combinado de aprendizagem de máquina e GPT

Benevento

Meirelles

2023

Regae: Rev. Gest. Aval. Educ.

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Por meio deste texto, apresenta-se resultados de pesquisa na qual se utilizou algoritmos de aprendizado de máquina combinado com o GPT para prever e melhorar o desempenho dos alunos. Essa abordagem pode ter impacto inovador na educação e na experiência de aprendizado dos alunos. O estudo teve abordagem quantitativa, com base em pesquisa experimental e técnicas variadas. Foram processados 900 alunos em 21 algoritmos. Os resultados indicaram uma ferramenta poderosa para prever e melhorar o desempenho dos alunos, combinado ao GPT, superando outros métodos. Conhecer as lacunas no conhecimento dos alunos e fornecer feedback personalizado, permite uma formação mais efetiva. Essa combinação pode ser uma ferramenta valiosa para aprimorar a educação.

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Section: Prever O Desempenho Dos Alunos -Pdaunclassified

Section: Algoritmos Tipos De Aprendizado E Ferramentasunclassified

Prever e melhorar o desempenho dos alunos com o uso combinado de aprendizagem de máquina e GPT

Benevento

Meirelles

2023

Regae: Rev. Gest. Aval. Educ.

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“…The classification accuracy always seizes the first look when a model is built for a classification problem as the number of instances predicted correctly from all predictions made, but the classification accuracy is not sufficient alone to evaluate a model, especially in case of imbalanced data classification. Therefore, we considered some other measurements such as sensitivity, precision, and F-measures [24]. The measurement equations used in model evaluation listed in Table 3 were: TP for True positives, TN for True negatives, FP for false positives, and FN for False negatives [25].…”

Section: Model Evaluationmentioning

confidence: 99%

Simulated annealing for SVM parameters optimization in student’s performance prediction

2021

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High education is an important and critical part of education all over the world. In last year, the world has been turned increasingly to online education due to the outbreak of the Covid-19 pandemic; therefore, improving this education system became an urgent matter. Online learning systems are a primal environment for acquiring educational data which can be from different sources, especially academic institutions. These data can be mainly used to analyze and extract utilizable information to help in understanding university students’ performance and identifying factors that affect it. To extract some meaningful information from these large volumes of data, academic organizations must mine the data with high accuracy. In this work, three different real datasets were selected, pre-processed, cleaned, and filtered for applying support vector machine (SVM) with multilayer perceptron kernel (MLP kernel) and optimize its parameters using simulated annealing (SA) algorithm to improve the objective function value. While examining the search space, SA has the advantage of escaping from local minima since it offers the chance for accepting the worse neighbor as a solution in a controlled manner. The results show that the designed system can determine the best SVM parameters using SA and therefore presents better model evaluation.

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“…The main aim of this application is predicting the students' academic failure to improve their learning and develop the educational process. Also, it helps stakeholders in education to improve the performance of students in future [7] [5]. The most used techniques for predicting student performance includes Bayesian classification, decision trees, neural network, rule based and feature selection.…”

Section: A Predicting Student Performancementioning

confidence: 99%

Educational Data Mining Applications and Techniques

Alshareef¹,

Alhakami²,

Alsubait³

et al. 2020

IJACSA

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Educational data mining (EDM) uses data mining techniques to analyze huge amounts of student data in the educational environments. The main purpose of EDM is to analyze and solve educational issues and, consequently, improve educational processes. With the emergence of EDM applications in the educational environments, several techniques have been identified to implement these applications. This paper reviews the relevant studies in EDM including datasets and techniques used in those studies and identifies the most effective techniques. The most prevalent applications include predicting student performance, detecting undesirable student behaviors, grouping students and student modeling. These applications aim to help decision makers in the educational institutions to understand student situations, improve students' performance, identify learning priorities for different groups of students and develop learning process. The prediction accuracy is selected as the evaluation criteria for the effectiveness of educational data mining techniques. The results show that Bayesian Network and Random Forest are the most effective techniques for predicting student performance, Social Network Analysis is the best technique for detecting undesirable student behaviors, Clustering and Social Network Analysis are the most effective techniques for grouping students and student modeling, respectively. This study recommends conducting more comprehensive and extended studies to evaluate the effectiveness of EDM techniques with an extended evaluation criteria.

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On Improving Student Performance Prediction in Education Systems using Enhanced Data Mining Techniques

Cited by 13 publications

References 2 publications

Prever e melhorar o desempenho dos alunos com o uso combinado de aprendizagem de máquina e GPT

Prever e melhorar o desempenho dos alunos com o uso combinado de aprendizagem de máquina e GPT

Simulated annealing for SVM parameters optimization in student’s performance prediction

Educational Data Mining Applications and Techniques

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