Navigating the digital divide: Gender, academic streams, and socio-economic influences on internet knowledge among undergraduates

The advent of the internet has significantly transformed the landscape of self-directed learning among students in higher education. However, disparities persist in the foundational skills required for effective internet use and computer operation among these students. To investigate the factors contributing to these variations in internet proficiency, this study titled “Navigating the Digital Divide: Gender, academic streams, and socio economic influences on internet knowledge among undergraduates” was conducted. Utilizing the Internet Knowledge Scale (IKS), developed by the researcher, alongside the Socio Economic Status Scale (SESS) developed by Rajeev Lochan Bharadwaj, a sample of 150 undergraduate students comprising both male and female participants from diverse academic disciplines and socio-economic backgrounds was analyzed. The research employed both descriptive and inferential statistical methods to draw conclusions. The findings revealed that academic streams and socio-economic status significantly influenced internet knowledge among students, while gender did not emerge as a determining factor. This underscores the need for targeted educational interventions that address these disparities, ensuring equitable access to digital literacy resources across different student demographics.

Authors

Institutions

Publication Details

Journal
International Journal of Advanced Academic Studies
Published
2025-08-01
DOI
https://doi.org/10.33545/27068919.2025.v7.i8a.1592
Primary Topic
ICT Impact and Policies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Navigating the digital divide: Gender, academic streams, and socio-economic influences on internet knowledge among undergraduates

Chiranjibi Behera
International Journal of Advanced Academic Studies
ICT Impact and Policies
article

Navigating the digital divide: Gender, academic streams, and socio-economic influences on internet knowledge among undergraduates

Chiranjibi Behera
article en

Abstract

The advent of the internet has significantly transformed the landscape of self-directed learning among students in higher education. However, disparities persist in the foundational skills required for effective internet use and computer operation among these students. To investigate the factors contributing to these variations in internet proficiency, this study titled “Navigating the Digital Divide: Gender, academic streams, and socio economic influences on internet knowledge among undergraduates” was conducted. Utilizing the Internet Knowledge Scale (IKS), developed by the researcher, alongside the Socio Economic Status Scale (SESS) developed by Rajeev Lochan Bharadwaj, a sample of 150 undergraduate students comprising both male and female participants from diverse academic disciplines and socio-economic backgrounds was analyzed. The research employed both descriptive and inferential statistical methods to draw conclusions. The findings revealed that academic streams and socio-economic status significantly influenced internet knowledge among students, while gender did not emerge as a determining factor. This underscores the need for targeted educational interventions that address these disparities, ensuring equitable access to digital literacy resources across different student demographics.

International Journal of Advanced Academic StudiesVol. 7(8)
Moog (United States) (US)
Quality Education
Openalex Percentile: Top 73%
ICT Impact and Policies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.