Gap Detection in Web-Based Adaptive Educational Systems

Content development for adaptive educational systems is known to be an error-prone task. Gaps can occur when the content is created, modified or when the context of its usage changes. This paper aims at improving the existing practises of learning content quality control in adaptive educational systems by automating detection and management of gaps. Several categories of gaps are identified to account for structural, linguistic, and semantic content inconsistencies in learning content collections. An effective filtering mechanism have been implemented in order to slice the collected gap data into categories that are relevant for the current authoring context. Evaluation of the developed tool demonstrates its utility.