A Study on Learner Analytics in Self-Regulated Learning for Working Professionals on Digital Platforms in Nashik District, Maharashtra

Self-regulated learning (SRL) has emerged as a critical competency for working professionals engaged in continuous education through digital platforms. As online learning proliferates in India, understanding how learner analytics (LA) can support and enhance SRL processes be-comes increasingly important for both platform designers and educators. This study investigates the relationship between learner analytics features and self-regulated learning behaviours among 200 working professionals enrolled in digital learning courses in Nashik District, Maharashtra. Employing a mixed-methods research design, the study combines quantitative survey data mea-sured on established SRL scales with qualitative insights from semi-structured interviews and platform log data analysis. Findings reveal that 67.5% of respondents demonstrate moderate to high SRL engagement, with goal setting (60%) and self-monitoring (65%) being the most fre-quently adopted strategies. However, only 38% regularly utilise learning analytics dashboards, and merely 35% adjust their study strategies based on analytics data. Pearson correlation analysis shows a significant positive relationship between SRL engagement scores and learning outcomes (r = 0.62, p < 0.001), and chi-square tests confirm that awareness of LA features is significantly associated with SRL strategy adoption (χ² = 14.83, p < 0.01). The study proposes a Learner Analytics Integration Framework that maps LA features to Zimmerman’s three-phase SRL model, offering actionable design recommendations for digital learning platforms targeting working professionals.