Simulation of Machine Learning Techniques to Predict Academic Performance

Chakrapani, Praveena; Chitradevi, D

doi:10.1109/icesic53714.2022.9783487

Cited by 3 publications

(1 citation statement)

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“…Random Forest generated the best results with an accuracy of 94.10%". Chakrapani and Chitradevi [55] analyzed "several classification algorithms for EDM in depth, in order to determine the most suitable attributes and the most efficient Machine Learning Model to accurately predict the academic performance of students and ensure academic success. From the simulated experiment, it has been observed that the SVM Linear Kernel Model gives the highest accuracy of 83.1% and the Decision Tree Model gives the lowest accuracy of 70% for the identical data set and identical attributes".…”

Section: Literature Reviewmentioning

confidence: 99%

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%