Technology Category
Machine Learning: Causal LearningCognitive Modeling & Cognitive Systems: AnalogyMultiagent Systems: Multiagent Learning
From Data to Actionable Understanding: A Learner-Centered Framework for Dynamic Learning Analytics
🤖 AI Summary
Current learning analytics dashboards (LADs) overemphasize data visualization while neglecting core cognitive processes—such as meaning-making and self-regulation—thereby limiting their effectiveness in supporting learners’ active engagement. To address this, we propose AUF, a learner-centered dynamic learning analytics framework. AUF introduces a novel multidimensional coupling model that integrates context awareness, dynamic meaning-making, adaptive feedback, and embedded metacognitive support, emphasizing real-time interactivity among components and personalized evolutionary trajectories. The framework is operationalized through four synergistic mechanisms: contextual modeling, a sensemaking strategy engine, an adaptive mechanism, and metacognitive prompting techniques. Our work yields a scalable theoretical framework and empirically grounded design principles. It provides both methodological guidance and empirical evidence for developing learning analytics tools that are more effective, equitable, and engaging—advancing the field beyond static, instructor-oriented dashboards toward truly learner-empowering systems.
Technology Category
Application Category
📝 Abstract
Learning Analytics Dashboards (LADs) often fall short of their potential to empower learners, frequently prioritizing data visualization over the cognitive processes crucial for translating data into actionable learning strategies. This represents a significant gap in the field: while much research has focused on data collection and presentation, there is a lack of comprehensive models for how LADs can actively support learners' sensemaking and self-regulation. This paper introduces the Adaptive Understanding Framework (AUF), a novel conceptual model for learner-centered LAD design. The AUF seeks to address this limitation by integrating a multi-dimensional model of situational awareness, dynamic sensemaking strategies, adaptive mechanisms, and metacognitive support. This transforms LADs into dynamic learning partners that actively scaffold learners' sensemaking. Unlike existing frameworks that tend to treat these aspects in isolation, the AUF emphasizes their dynamic and intertwined relationships, creating a personalized and adaptive learning ecosystem that responds to individual needs and evolving understanding. The paper details the AUF's core principles, key components, and suggests a research agenda for future empirical validation. By fostering a deeper, more actionable understanding of learning data, AUF-inspired LADs have the potential to promote more effective, equitable, and engaging learning experiences.
Problem
Research questions and friction points this paper is trying to address.
LADs lack models for supporting learners' sensemaking and self-regulation
Existing frameworks isolate situational awareness and metacognitive support
Need for adaptive LADs to personalize learning strategies dynamically
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates situational awareness and dynamic sensemaking strategies
Emphasizes adaptive mechanisms and metacognitive support
Creates personalized and adaptive learning ecosystems
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