User-Centered Course Reengineering: An Analytical Approach to Enhancing Reading Comprehension in Educational Content
Madjid Sadallah
TL;DR
The paper tackles the challenge of ensuring digital educational content supports effective reading and learning by introducing a usage-based document reengineering framework. It combines document engineering, reading comprehension theory, and learning analytics to extract reading traces, derive session-based indicators, detect comprehension issues, and propose targeted revisions via the CoReaDa dashboard. The framework is instantiated and evaluated on a major European e-learning platform, showing that dynamic, session-aware thresholds and indicators can reveal actionable content-improvement opportunities and empower authors to revise material data-drivenly. Findings indicate positive author reception, meaningful insights for course revision, and potential for broader adoption, while also highlighting the need for larger-scale validation and integration with learner feedback to maximize impact.
Abstract
Delivering high-quality content is crucial for effective reading comprehension and successful learning. Ensuring educational materials are interpreted as intended by their authors is a persistent challenge, especially with the added complexity of multimedia and interactivity in the digital age. Authors must continuously revise their materials to meet learners' evolving needs. Detecting comprehension barriers and identifying actionable improvements within documents is complex, particularly in education where reading is fundamental. This study presents an analytical framework to help course designers enhance educational content to better support learning outcomes. Grounded in a robust theoretical foundation integrating learning analytics, reading comprehension, and content revision, our approach introduces usage-based document reengineering. This methodology adapts document content and structure based on insights from analyzing digital reading traces-interactions between readers and content. We define reading sessions to capture these interactions and develop indicators to detect comprehension challenges. Our framework enables authors to receive tailored content revision recommendations through an interactive dashboard, presenting actionable insights from reading activity. The proposed approach was implemented and evaluated using data from a European e-learning platform. Evaluations validate the framework's effectiveness, demonstrating its capacity to empower authors with data-driven insights for targeted revisions. The findings highlight the framework's ability to enhance educational content quality, making it more responsive to learners' needs. This research significantly contributes to learning analytics and content optimization, offering practical tools to improve educational outcomes and inform future developments in e-learning.