EFFECTS OF VIRTUAL PRIVATE SOCIAL NETWORKING IN ACADEMIC PERFORMANCE OF STUDENTS

Title

EFFECTS OF VIRTUAL PRIVATE SOCIAL NETWORKING IN ACADEMIC PERFORMANCE OF STUDENTS

Creator

Annamalai A.; Poonia R.C.; Shanmugasundaram S.

Description

A virtual private social network (VPSN) is generated automatically amongst peers using a social media app to build ties. One of the most significant repercussions of students' excessive usage of social networking sites is a decline in their academic performance. In a study of medical students, social media and the internet were shown to harm students' academic performance and classroom attentiveness. An increasing number of studies link the use of social media to poorer academic performance, such as fewer students doing their assignments and lower test scores. Students who receive specialised training in deep learning will have the superior cognitive abilities needed to succeed in today's more cognitively demanding workplaces. It teaches children to be critical thinkers, productive members of society, and active participants in a democratic society. As a perceptron used in image recognition and processing, a convolutional neural network (CNN) processes pixel data from social networks. A CNN uses multiplayer perception to lessen the processing needs of pupils. Humans and neurons make up the VPSN-CNN network, which the article explains. Neurons generate dendrites and axons to receive and transmit signals, while humans engage with long-reaching telecommunication equipment or biological communication systems. These will help remember, learn, unlearn, and relearn what has already been learned. In courses where social networking sites were utilised in addition to traditional teaching methods, most students reported feeling more socially engaged and more positive about their educational experiences. Students' and instructors' concerns regarding the educational usage of social media are addressed with recommendations for further study and practice in better performance and accuracy for student's data secure and comparison with existing methods. 2023 Little Lion Scientific. All rights reserved.

Source

Journal of Theoretical and Applied Information Technology, Vol-101, No. 4, pp. 1579-1593.

Date

2023-01-01

Publisher

Little Lion Scientific

Subject

CNN; Deep learning; Social network; Students

Coverage

Annamalai A., Amity Institute Information Technology, Amity University Rajasthan, Rajasthan, Jaipur, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India; Shanmugasundaram S., Department of Engineering, Faculty of Engineering & Technology, Botho University, Gaborone, Botswana, PO Box 501564, Kgale KO, Near Game City, Gaborone, Botswana

Rights

Restricted Access

Relation

ISSN: 19928645

Format

Online

Language

English

Type

Article

Identifier

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153087456&partnerID=40&md5=b3d67a8a9a2fe5239c66f7004a3fed7b