International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 3 May-June 2024 Submit your research before last 3 days of June to publish your research paper in the issue of May-June.

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 The objective is to categorize student outcomes into four classes: Pass, Distinction, Withdrawn, and Fail.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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