Models and Tools for Usage-based e-Learning Documents Reengineering
Abstract
Providing high-quality content is of utmost importance to drive successful reading. Besides, designing documents that are received the way the author wishes has always been difficult, and the digital world increases this difficulty by multiplying the possibilities related to mixed medias and interactivity. This compels authors to continuously review the delivered content to meet readers' needs. Yet it remains challenging for them to detect the comprehension barriers that may exist within their documents, and to identify how these latter can be improved accordingly. This compels authors to continuously review the delivered content to meet readers' needs. Yet it remains challenging for them to detect the comprehension barriers that may exist within their documents, and to identify how these latter can be improved accordingly. In this thesis, we focus on an educational context, where reading is a fundamental activity and the basis of many other learning activities. We propose a learning analytics approach for assisting course authors to maintain their courses to sustain learning. The proposals are based on theoretical background originated from research on learning analytics, reading comprehension and content revision. We advocate \usage-based document reengineering", a process defined as a kind of reengineering that changes document content and structures based on the analysis of readers' usages as recorded in their reading traces. We model reading activity using the concept of reading-session and propose a new session identification method. Using learners' reading sessions, a set of indicators related to different aspects of the reading process are computed and used to detect comprehension issues and to suggest corrective content revisions. The results of the analytics process are presented to authors through a dashboard empowered with assistive features. We instantiate our proposals using the logs of a major e-learning platform, and validate it through a series of studies. The results show the effectiveness of the approach and dashboards in providing authors with guidance in improving their courses accordingly.
Description
Keywords
Document reengineering, e-Learning, Learning analytics, Learning dashboard, Reading monitoring, Reading Comprehension, Document Revision, Web log mining