PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses

2025-07-24

AI Summary

Traditional learning analytics (LA) primarily focuses on single courses or individual learners, limiting its ability to model course interdependencies and longitudinal academic trajectories—thereby constraining scalability. To address this, we propose the Panoramic Academic Learning Map (PALM), the first framework integrating course knowledge graphs with cross-semester learning behavior data to enable multi-level, cross-course academic progression visualization. PALM unifies multi-source educational data, supports dynamic visual analytics, and delivers personalized learning dashboards, bridging granular course performance with holistic learning pathway insights. Empirical evaluation demonstrates that PALM significantly enhances students’ perceived behavioral control in learning planning and reflection. Moreover, it outperforms existing LA platforms in visual design quality and usability. By enabling scalable, fine-grained, and longitudinal academic analytics, PALM provides robust technical infrastructure for self-regulated learning.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Multiagent Systems: Multiagent Learning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

This study proposes and evaluates the PAnoramic Learning Map (PALM), a learning analytics (LA) dashboard designed to address the scalability challenges of LA by integrating curriculum-level information. Traditional LA research has predominantly focused on individual courses or learners and often lacks a framework that considers the relationships between courses and the long-term trajectory of learning. To bridge this gap, PALM was developed to integrate multilayered educational data into a curriculum map, enabling learners to intuitively understand their learning records and academic progression. We conducted a system evaluation to assess PALM's effectiveness in two key areas: (1) its impact on students' awareness of their learning behaviors, and (2) its comparative performance against existing systems. The results indicate that PALM enhances learners' awareness of study planning and reflection, particularly by improving perceived behavioral control through the visual presentation of individual learning histories and statistical trends, which clarify the links between learning actions and outcomes. Although PALM requires ongoing refinement as a system, it received significantly higher evaluations than existing systems in terms of visual appeal and usability. By serving as an information resource with previously inaccessible insights, PALM enhances self-regulated learning and engagement, representing a significant step beyond conventional LA toward a comprehensive and scalable approach.

Problem

• Addresses scalability challenges in learning analytics across courses
• Integrates curriculum-level data for long-term learning trajectory insights
• Enhances student awareness of learning behaviors and outcomes

Innovation

• Integrates curriculum map with learning analytics
• Visualizes learning histories and statistical trends
• Enhances self-regulated learning and engagement