The Edu-Influence Evaluation Matrix (EEM): Reframing Credibility in Digital Pedagogy

Abstract

The expansion of platform mediated learning has given rise to educator-influencers who distribute pedagogic, affective, visibility-oriented forms of educational engagement across digital ecosystem. While existing scholarship has examined influencer culture and social media in education separately, there has been limited research on how educational authority, credibility and pedagogic influence are constructed across platforms. This study addresses that gap through a qualitative exploratory analysis of select educator influencers operating across YouTube, Instagram and LinkedIn. Drawing on theories of micro-celebrity, networked scholarship and affective publics, the study examines how pedagogic intent, visibility practices and credibility signals are operationalised within platform specific environments. Using structured qualitative content analysis, this study analyses a purposive sample of Indian educator-influencers while drawing selectively on international educator influencers for contextual references. The findings demonstrate clear platform differentiation.: YouTube emerged as the primary site of pedagogic depth and structured instruction; Instagram functioned as affective-relational ecosystem centred on aspiration, community engagement while LinkedIn continued to operate as a space of institutional legitimacy and professional growth. The study further identifies a distinction between learner-facing and professional-facing forms of educational influence, revealing how pedagogic, relational and visibility labour are strategically distributed across platforms. From these empirical patterns, the study develops the Edu-Influence Evaluation Matrix (EEM), comprising three analytical domains: pedagogic intent, visibility practices and credibility signals. The EEM offers a platform-sensitive framework for evaluating educational influence beyond engagement metrics, with implications for researchers, educational institutions and policymakers in increasingly algorithmic learning environments.