Machine Learning Techniques for Analysing Students Feedback Towards Quality Management in Higher Education

Shaifali Garg, Malik Jawarneh, Meenakshi, Sammy F.

2023

Abstract

The area of research referred to as educational data mining is one that makes use of data mining, machine learning, and statistics in order to investigate material that has been especially obtained from educational settings. The goal of the learning and teaching process is to provide pupils the best possible experience they can have in terms of learning and comprehending the material being taught. Educational data mining can be used for a variety of purposes, including predicting student performance and identifying students who are at risk, determining important concerns in the learning patterns of various groups of students, increasing pass-out rates, accurately assessing the performance of the institution, making the most of campus resources, and optimising the renewal of subject curriculum. This article provides machine learning techniques for analysing students feedback towards quality management in higher education. Student feedback data set is preprocessed to remove noise. Then student feedback data is analysed using SVM, ANN and random Forest algorithm. Performance of SVM algorithm is found better for analyzing student feedback data for overall quality improvement in higher educational institutions.

Paper Citation

in Bibtex Style

@conference{ai4iot23,
author={Shaifali Garg and Malik Jawarneh and Meenakshi and Sammy F.},
title={Machine Learning Techniques for Analysing Students Feedback Towards Quality Management in Higher Education},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={309-313},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012615200003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Machine Learning Techniques for Analysing Students Feedback Towards Quality Management in Higher Education
SN - 978-989-758-661-3
AU - Garg S.
AU - Jawarneh M.
AU - Meenakshi.
AU - F. S.
PY - 2023
SP - 309
EP - 313
DO - 10.5220/0012615200003739
PB - SciTePress

in Harvard Style

Garg S., Jawarneh M., Meenakshi. and F. S. (2023). Machine Learning Techniques for Analysing Students Feedback Towards Quality Management in Higher Education. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 309-313. DOI: 10.5220/0012615200003739