Logic-based Learner Assessment on the Semantic Web

摘要

We are currently witnessing the transformation of static e-learning environments to adaptive ones, along with the gradual incorporation of semantic web technologies. These technologies enable more sophisticated user and content modelling, which in turn gives adaptation mechanisms new potential. The role of learner assessment newly formed setting is prevailing, as assessment results are used as input to the adaptation mechanisms and are also used to update learner profiles. This paper presents an overall categorization of assessment methods used in the e-learning domain and employs first-order logic to describe a number of indicative assessment mechanisms by proposing a set of atoms, predicates and rules to express the logic that produces the assessment decisions and gives indicative examples of transforming first-order logic rules to TRIPLE, thus allowing easier implementation of assessment strategies with semantic web technologies and provides a formal way for comparing alternative assessment schemes.