2021 IEEE International Conference on Automatic Control &Amp; Intelligent Systems (I2CACIS) 2021
DOI: 10.1109/i2cacis52118.2021.9495862
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Predicting University's Students Performance Based on Machine Learning Techniques

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Cited by 20 publications
(9 citation statements)
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“…This is a quick synopsis of machine learning using decision trees. A decision tree is a type of hierarchical tree structure in which a decision or testing on a characteristic is represented by each node, the test result is represented by each branch, and the ultimate decision or goal variable is represented by each leaf node [32].…”
Section: A Random Forestmentioning
confidence: 99%
“…This is a quick synopsis of machine learning using decision trees. A decision tree is a type of hierarchical tree structure in which a decision or testing on a characteristic is represented by each node, the test result is represented by each branch, and the ultimate decision or goal variable is represented by each leaf node [32].…”
Section: A Random Forestmentioning
confidence: 99%
“…Ahmed et al [15] proposed an approach to predict university students' performance in final exams using the algorithm (GBDT) which is a machine learning tech-nology used for regression, classification, and ranking tasks, and is part of the Boosting method family.…”
Section: Related Workmentioning
confidence: 99%
“…In many prior EDM and LA studies, researchers have employed a type of ML algorithm, known as Naïve Bayes, to forecast student performance in various STEM settings (see Shahiri and Husain, 2015;Ahmed et al, 2021;Perez and Perez, 2021 for examples). In recent systematic literature reviews, Shafiq et al (2022), Peña-Ayala (2014), and Baashar et al (2021), found that Naïve Bayes was used in 35%, 20%, and 14% of education studies surveyed, respectively.…”
Section: Application To Stem Educational Settings and ML Assessmentmentioning
confidence: 99%