Automated Recommendations for Revising Lecture Slides Using Reading Activity Data

Abstract

The use of digital textbooks in education provides valuable data on student reading behavior that can help educators refine their course materials and instructional design for future iterations. Previous studies have explored methods for extracting important evidence from this data, but they require manual intervention. By automating these methods, this paper introduces an end-to-end system capable of extracting evidence from e-book data and providing recommendations for slides' content review based on this evidence. Our system incorporates information about reading preferences into the evidence-extraction process and implements Large Language Models (LLMs) for automatic interpretation. Six teachers evaluated our proposed system indicating a promising level of effectiveness, while also highlighting areas for future improvement to ensure a successful classroom implementation. These include considerations for improving the actionability of recommendations, improving the identification of content that needs refinement, and improving the performance of LLMs.