Generalized metrics for the analysis of E-learning personalization strategies

For personalizing E-learning, several different strategies and characteristics can be used and considered by teachers and course authors/designers. In order to make appropriate decisions on how to best implement personalized E-learning, this paper focuses on the question: How to foresee personalization strategies that are appropriate for particular courses? To answer this question, we present an approach for recommending personalization strategies based on the learning objects included in the course as well as on how well they support particular combinations of learners' characteristics. In particular, the paper presents generalized metrics which support teachers for analyzing and comparing personalization strategies, as well as deciding which one should be applied for personalizing each course. The approach was validated through experiments in order to test its feasibility and success when applied to a large number of learning objects and learners' characteristics.