Automatic extraction of core learning goals and generation of pedagogical sequences through a collection of digital library resources

A key challenge facing educational technology researchers is how to provide structure and guidance when learners use unstructured and open tools such as digital libraries for their own learning. This work attempts to use computational methods to identify that structure in a domain independent way and support learners as they navigate and interpret the information they find. This article highlights a computational methodology for generating a pedagogical sequence through core learning goals extracted from a collection of resources which in this case, are resources from the Digital Library for Earth System Education (DLESE). This article describes how we use the technique of multi-document summarization to extract the core learning goals from the digital library resources and how we create a supervised classifier that performs a pair-wise classification of the core learning goals; the judgments from these classifications are used to automatically generate pedagogical sequences. Results show that we can extract good core learning goals and make pair-wise classifications that are up to 76% similar to the pair-wise classifications generated from pedagogical sequences created by two science education experts. Thus we can dynamically generate pedagogically meaningful learning paths through digital library resources.