Enhancing Educational Assessment: Predicting and Visualizing Student Performance using EDA and Machine Learning Techniques

R  Parkavi; P  Karthikeyan; S Sujitha  and A  Sheik Abdullah

doi:10.16920/jeet/2024/v37is2/24046

Journal of Engineering Education Transformations

DOI: 10.16920/jeet/2024/v37is2/24046

Year: 2024, Volume: 37, Issue: Special Issue 2, Pages: 240-245

Original Article

Enhancing Educational Assessment: Predicting and Visualizing Student Performance using EDA and Machine Learning Techniques

R. Parkavi¹, P. Karthikeyan², S.Sujitha³ and A. Sheik Abdullah⁴

^1,2,3Department of Information Technology, Thiagarajar College of Engineering, Madurai
⁴School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Chennai

*Corresponding Author
Email: [email protected]
[email protected]
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Received Date:12 January 2024, Accepted Date:18 January 2024, Published Date:20 January 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Abstract— Educational institutions must manually evaluate student's performance, which requires a significant amount of faculty time and effort. Teachers can easily monitor and document student's learning behavior if they utilize a learning management system (LMS). It can be utilized by coaching sessions or educational institutions to quickly analyze student's performance. Due to the enormous number of pupils, huge data must be analyzed; teachers frequently encounter challenges. The qualities of learners who are students are described analytically in this analysis. In this work, student's cognitive and psychomotor skills are predicted and visualized using Exploratory Data Analysis (EDA) and Machine Learning techniques like KNN and Multiple Regressions. The maximum accuracy of 99% has been obtained in Multiple Regression.

Keywords—Analytics; Education; Learning Management System; Performance; Students;