Designing Learning Analytics Dashboards for Digital Learning Environments: Investigating Learner Preferences, Usage, and Self-Efficacy

Abstract

This dissertation, a product of the European Union's CHARMING project, investigates the intersection of technology and learning, focusing on the design of learning analytics for lifelong learning. It emphasizes the importance of effective learning design and the innovative use of technology in digital learning environments. Chapter 1 presents the problem statement, highlighting the knowledge gap related to learning analytics design and the overarching research question: How does learning analytics dashboard (LAD) design influence learner preferences, interaction, and self-efficacy in training and education? Chapter 2 investigates workplace learner preferences for LADs designed for different phases of the self-regulated learning (SRL) cycle. The study reveals a preference for progress reference frames before and after task performance, while social reference frames are least preferred. Chapter 3 examines the impact of LADs with progress and social reference frames on occupational self-efficacy in virtual reality simulation-based training environments. The findings suggest that both reference frames could elicit equal change in self-efficacy, with social reference frames potentially inducing more significant change. Chapter 4 analyzes log-file data to understand chemical plant employees' engagement with LADs. The results indicate that progress reference frames might foster mastery goal orientation behaviors, while social reference frames seem to promote performance goal orientation behaviors. Chapter 5 investigates the impact of LAD reference frame type and direction of comparison on academic self-efficacy among university students. The findings highlight the influence of both comparison type and direction on changes in academic self-efficacy. Chapter 6 discusses the main research findings, theoretical and practical implications, limitations, and future research opportunities. The dissertation contributes to the understanding of LAD design and its influence on learning-related variables, providing valuable insights for educational stakeholders and researchers. This dissertation advances the understanding of learning analytics dashboard design and its impact on learner preferences, interaction, and self-efficacy in various educational contexts. The findings provide a foundation for future research and the development of more effective digital learning environments.