Trust, Transparency, and Learner Data Use in Community Education Platforms

Abstract

This study examines how trust and transparency influence learner acceptance of data use in community education platforms. The study is designed to collect data from approximately 3,000 learners using 15 community education platforms, together with platform policy documents, consentforms, user activity records, and administrator interviews. The dataset is expected to include around 3,000 learner questionnaires, 150 platform policy documents, 500 consent-form samples, 200,000 anonymized platform-use logs, and 45 administrator interviews. The study measures data-use transparency, consent clarity, privacy concern, perceived institutional responsibility, algorithmic explanation, platform trust, willingness to share learning data, and continued platform-use intention. Quantitative methods include reliability testing, confirmatory factor analysis, structural equation modeling, logistic regression, moderation analysis, and multi-group comparison to examine whether transparency reduces privacy concerns and increases learner trust. Platform-use logs are also analyzed to compare self-reported trust with actual participation behavior, including login frequency, course completion, feedback submission, and data-sharing choices. The innovation of this study lies in connecting learner data governance with measurable trust and behavioral acceptance, showing that transparent data practices are not only ethical requirements but also important conditions for sustainable platform-based community education