Data-Driven Evaluation of Course Outcomes Using ML Models

DOI

10.2991/978-94-6463-738-0_24How to use a DOI?

Keywords

Machine Learning in Education; Course Outcome Analysis (COA); Classification Models; Curriculum Enhancement; Educational Data Analysis

Abstract

Course Outcome Analysis (COA) is crucial for evaluating and improving educational effectiveness. This study aims to classify course outcomes and identify areas for improvement using ML models like D.T. (Decision Tree), R.F. (Random Forest), and L.R. (Logistic Regression) models got applied to historical student performance and evaluation data. The methodology involved training and testing these models to determine their predictive accuracy. Key findings indicate that the Random Forest model outperforms others in classifying course outcomes, offering a reliable, data-driven approach to academic planning. This framework facilitates continuous improvement of courses, optimizing the learning experience and aligning outcomes with educational objectives.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Vaibhav Agarwal
AU  - Vivek Tiwari
AU  - Harshil Kanakia
PY  - 2025
DA  - 2025/06/22
TI  - Data-Driven Evaluation of Course Outcomes Using ML Models
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 286
EP  - 298
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_24
DO  - 10.2991/978-94-6463-738-0_24
ID  - Agarwal2025
ER  -