Predicting Student Performance Using Classification Data Mining Techniques

Shingari, Isha; Kumar, Dinesh

doi:10.26438/ijcse/v6i7.4348

Cited by 5 publications

(7 citation statements)

References 1 publication

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“…The techniques of machine learning used was made up of: Naïve Bayes, Artificial Neural Network, Logistics Regression and Decision Tree. The ANN model attained the best accuracy of all the classifiers that is equal to 77.04% as compared to Naïve Bayes model which had lowest accuracy that is of 66.52% Soni et al [32] prepared "a model which analysed the performance of pupils from their last output using Algorithms of classification such as: Naïve Bayes, Decision Tree, and Support Vector Machine for students' performance prediction. For the extraction process, twenty (20) out of the 48 features using the classifiers (NB, SVM and Decision Tree) were selected to analyse the influence of each feature for predicting the performance of students.…”

Section: Literature Reviewmentioning

confidence: 99%

“…[9], [19], [22], [25], [28], [29], [31], [32], [35], [36], [38], [41], [46], [49], [51] [25], [29], [46] 4 support vector machine 15 Supervised learning…”

Section: Classification References Best Performancementioning

confidence: 99%

“…[9], [11], [18], [29], [32]- [36], [38], [39], [45], [49], [51] [9], [32], [49] 5 Random Forest 11 Supervised learning [19], [20], [22], [28], [38], [41], [44], [45], [47], [48], [51] [28], [38], [45],…”

Section: Classification References Best Performanceunclassified

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Predicting Students’ Performance Using Machine Learning Algorithms: A Review

Oppong

2023

AJRCoS

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Educational Data Mining is a discipline focused on developing ways for studying the unique and increasingly large-scale data generated by educational settings and applying those methods to better understand students and the environments in which they learn. Predicting student performance is one of the most critical concerns in educational data mining, which is gaining popularity. Student performance prediction attempts to forecast a student's grade before enrolling in a course or completing an exam. The goal of this paper is to present a systematic literature review on predicting student performance using machine learning techniques and how the prediction algorithm can be used to identify the most important attribute(s) in a student's data. The study showed that neural networks is the most used classifier for predicting students’ academic results and also provided the best results in terms of accuracy. Also, 87% of the algorithms used were supervised learning as compared to 13% for unsupervised learning and 59% of the studies employed various feature selection methods to improve the performance of the machine learning models.

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Section: Literature Reviewmentioning

confidence: 99%

“…[9], [19], [22], [25], [28], [29], [31], [32], [35], [36], [38], [41], [46], [49], [51] [25], [29], [46] 4 support vector machine 15 Supervised learning…”

Section: Classification References Best Performancementioning

confidence: 99%

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Predicting Students’ Performance Using Machine Learning Algorithms: A Review

Oppong

2023

AJRCoS

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“…Algoritmos de AM têm se mostrado úteis na PDA para prever desempenhos, melhorar metodologias de ensino e identificar métodos instrucionais adequados aos alunos (Belachew;Gobena, 2017;Ofori et al, 2020;Soni et al, 2018). A AM é amplamente utilizada para estudos sobre PDA, processando notas para prever desempenho.…”

Section: Algoritmos Tipos De Aprendizado E Ferramentasunclassified

“…A AM é amplamente utilizada para estudos sobre PDA, processando notas para prever desempenho. Isso requer grandes conjuntos de dados, cujas entradas são calculadas para gerar saídas sobre a eficiência da PDA, com base em técnicas e algoritmos específicos (Belachew;Gobena, 2017;Ofori et al, 2020;Soni et al, 2018).…”

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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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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Analysis of students performance based on the assessments using novel classification technique of decision tree comparing with logistic regression

Krishna,

Ashokkumar

2024

AIP Conference Proceedings

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Predicting Student Performance Using Classification Data Mining Techniques

Cited by 5 publications

References 1 publication

Predicting Students’ Performance Using Machine Learning Algorithms: A Review

Predicting Students’ Performance Using Machine Learning Algorithms: A Review

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

Analysis of students performance based on the assessments using novel classification technique of decision tree comparing with logistic regression

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