Exploring the Learning Analytics of Skill-based Course using Machine Learning Classification Models

Author(s)	A.Vinitha, M.Madhusudhan
Country	India
Abstract	This study explores learning analytics of a skill-based course using various machine learning classification models, including Random Forest, Logistic Regression, CatBoost, Support Vector Classification (SVC), and Naïve Bayes. The objective is to categorize student outcomes into four classes: Pass, Distinction, Withdrawn, and Fail. The research contributes to the growing body of knowledge in learning analytics and machine learning applications in education. The findings from this investigation offer educators and academic institutions a robust framework for early identification of students at risk of underperformance or withdrawal, thereby enabling timely intervention to enhance student success in skill-based courses
Keywords	Random Forest, Logistic Regression, CatBoost, Support Vector Classification (SVC), and Naïve Bayes,Mlp, Lda,Passive aggressive classifier
Field	Computer > Data / Information
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-05-11
Cite This	Exploring the Learning Analytics of Skill-based Course using Machine Learning Classification Models - A.Vinitha, M.Madhusudhan - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.19922
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.19922
Short DOI	https://doi.org/gttvdn

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International Journal For Multidisciplinary Research