Computational foundations for personalizing instruction with digital libraries

Abstract

This paper describes our progress towards automating adaptive personalized instruction based on student conceptual understandings using digital libraries. The reported approach merges conversational learning theory with advances in natural language processing to enable personalized pedagogical interactions. Multi-document summarization techniques serve as the computational basis to process digital library resources and automatically construct a rich domain model on earthquakes and plate tectonics for high school age learners. Shallow semantic analysis and graph-based techniques are used to computationally diagnose student understandings that enable conceptual personalizations integrating digital library resources. The evaluation of the implemented algorithms indicates that digital libraries may serve as knowledge platforms to support the automated construction of rich domain models and the diagnosis of student conceptual understandings. Furthermore, this approach introduces a novel and effective alternative to prior work in adaptive learning environments in terms of scalability and portability, thus tackling important challenges associated with supporting personalized instruction using digital libraries.