User-Centered Course Reengineering: An Analytical Approach to Enhancing Reading Comprehension in Educational Content

• URL: http://arxiv.org/abs/2412.11944v1
• Date: Mon, 16 Dec 2024 16:26:20 GMT
• Title: User-Centered Course Reengineering: An Analytical Approach to Enhancing Reading Comprehension in Educational Content
• Authors: Madjid Sadallah,
• Abstract summary: This study presents an analytical framework to help course designers enhance educational content to better support learning outcomes. Our approach adapts document content and structure based on insights from analyzing digital reading traces-interactions between readers and content. Our framework enables authors to receive tailored content revision recommendations through an interactive dashboard.
• Score: 0.0
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• 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.

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