Easy precision matrix ML based algorithm for student academic performance improvement

• Published in: AIP conference proceedings Vol. 2831; no. 1
• Main Authors: Balamurugan, R., Nirmala, K.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 20.09.2023
• Subjects: Academic achievement; Algorithms; Decision trees; Machine learning; Performance evaluation; Training
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0166234

Easy precision is obtained using a Machine Learning algorithm in student’s academic performance analysis. If the semester result for the student is above certain grade, then the student obtained good academic result. On the other hand, if the test for the student is below grade 3 then he or she needs some training or counselling. To improve the students performance and to achieve good grade can be carried out by a machine learning algorithm. In the study of a student’s achievement, a Machine Learning method provides easy precision. If a student’s semester grade is above a given level, the student has done well academically. If the student’s test score is less than a grade three, he or she will need some training or counselling. A machine learning algorithm is used to improve a student’s performance and help them to get a good mark. Five methods were utilised to evaluate student performance in this paper: Random Forest algorithm, SVC (Space Vector Machine), decision tree, logistic regression, and Nave Bayes. In comparison to other methods, Decision Tree has a high level of accuracy. It is evident from this research that we might simply train the pupils in the objective aspect by applying machine learning methods.