Student's Performance Prediction Using Modified PSO

Title

Student's Performance Prediction Using Modified PSO

Creator

Kumari R.; Kumar S.

Description

In the present-day education system, evaluating students' performance is very important. The prediction of student performance is helpful to students and instructors in maintaining a student's progress. Presently, institutions are implementing Continuous Evaluation Systems. This system is beneficial for improving the students' performance and helps increase their regularity in their studies. This work proposed a new version of the particle swarm optimization algorithm to classify students into different categories based on their performance with the help of various factors. The proposed model modified the strategy to increase the optimization algorithm's efficiency. This model deals with the bag of features with the modified optimization algorithm to recognize students' performance based on 33 features. These features are used for training, validation, and testing of the student's performance and generate a classification report which includes F-measure, recall, precision, and accuracy. Based on the classification report, the proposed model is compared with the existing methods, and the better performance of the proposed model is ascertained. 2023 IEEE.

Source

Proceedings - 2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023, pp. 1015-1020.

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Bag-of-features; Machine Learning Algorithm; Optimization techniques; Student Performance

Coverage

Kumari R., Icfai Business School (IBS) Bangalore, Off-Campus Center of Icfai Foundation for Higher Education (IFHE) University, Bangalore, India; Kumar S., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 979-835031006-1

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/AIC57670.2023.10263967

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174510263&doi=10.1109%2fAIC57670.2023.10263967&partnerID=40&md5=ebc04261d60bf7e802c7cfb4133a117c