Integrated Analysis of Student Surveys with Machine-Learning

Neli Arabadzhieva-Kalcheva, Maya Todorova, Evgeniya Rakitina-Qureshi, Ivo Rakitin, Hristo Nenov, Dimitrichka Nikolaeva

2025

Abstract

This paper presents an integrated analysis of student survey data using machine learning methods. The main objective is to evaluate and compare the effectiveness of different algorithms in multi-class classification tasks related to student satisfaction. Ten widely used algorithms were applied: Decision Tree, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Logistic Regression, Linear Support Vector Machine, Support Vector Machine with RBF kernel, k-Nearest Neighbours, Gaussian Naïve Bayes, and Multilayer Perceptron. The evaluation was conducted using 5-fold cross-validation and four standard performance metrics: accuracy, precision, recall, and F1-score. Experimental results show that ensemble tree-based methods, particularly Gradient Boosting, Random Forest, and Histogram-based Gradient Boosting, achieved the highest performance across all metrics.

Paper Citation

in Harvard Style

Arabadzhieva-Kalcheva N., Todorova M., Rakitina-Qureshi E., Rakitin I., Nenov H. and Nikolaeva D. (2025). Integrated Analysis of Student Surveys with Machine-Learning. In Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS; ISBN 978-989-758-783-2, SciTePress, pages 266-270. DOI: 10.5220/0014328300004848

in Bibtex Style

@conference{iceeecs25,
author={Neli Arabadzhieva-Kalcheva and Maya Todorova and Evgeniya Rakitina-Qureshi and Ivo Rakitin and Hristo Nenov and Dimitrichka Nikolaeva},
title={Integrated Analysis of Student Surveys with Machine-Learning},
booktitle={Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS},
year={2025},
pages={266-270},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014328300004848},
isbn={978-989-758-783-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS
TI - Integrated Analysis of Student Surveys with Machine-Learning
SN - 978-989-758-783-2
AU - Arabadzhieva-Kalcheva N.
AU - Todorova M.
AU - Rakitina-Qureshi E.
AU - Rakitin I.
AU - Nenov H.
AU - Nikolaeva D.
PY - 2025
SP - 266
EP - 270
DO - 10.5220/0014328300004848
PB - SciTePress