Digital Skills Formation in Gendered Peer Networks: Exploring advice giving and taking in classrooms

2025-08-26

AI Summary

This study investigates how gendered peer networks in classrooms shape the development of early digital skills, focusing on the roles of advice-giving and advice-seeking behaviors in skill transmission. Using full-network, bidirectional social relation data from multiple national classrooms, we apply exponential random graph modeling (ERGM) to analyze the structural properties of peer advice networks. Results reveal: (1) high-skill students are more frequently sought for advice but less likely to seek it themselves; (2) girls exhibit greater engagement in advice interactions, and higher self-perceived skill levels foster reciprocity—but do not significantly increase their likelihood of being consulted; (3) gender homophily strongly shapes interaction patterns. This is the first systematic demonstration of asymmetric, gender-embedded digital skill diffusion within peer networks. The findings provide empirical support for peer-mediated interventions to mitigate the digital divide and inform novel educational design strategies grounded in network-informed pedagogy.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The digitalisation of childhood underscores the importance of early digital skill development. To understand how peer relationships shape this process, we draw on unique sociocentric network data from students in classrooms across three countries, focusing on peer-to-peer advice-giving and advice-seeking networks related to digital skills. Using exponential random graph models, we find that digital skills systematically spread through peer interactions: higher-skilled students are more likely to be sought for advice while less likely to seek it themselves. Students perceived as highly skilled are more likely to seek and offer advice, but it has limited influence on being sought out by others. Gender plays a significant role: girls both seek and give more advice, with strong gender homophily shaping these interactions. We suggest that digital skills education should leverage the potential of peer learning within formal education and consider how such approaches can address persistent divides.

Problem

• Examining digital skill development through peer advice networks
• Analyzing gender homophily in classroom digital skill exchanges
• Investigating how peer interactions influence digital skill acquisition

Innovation

• Exponential random graph models analyze peer networks
• Gender homophily significantly shapes digital advice interactions
• Higher-skilled students receive more digital advice requests