Evaluation of APeLS - an adaptive eLearning service based on the multi-model, metadata-driven approach

Abstract

peer-reviewedThe evaluation of learner and tutor feedback is essential in the production of high quality personalized eLearning services. There are few evaluations available in the Adaptive Hypermedia domain relative to the amount of research interest this domain is attracting. Many of the papers in this domain focus on the technological design of systems without justifying the designs through the lessons learned from evaluations. This paper evaluates the usability and effectiveness of using the multi-model, metadata-driven approach for producing rich adaptive eLearning solutions that remain content and domain independent. Through this independence, the eLearning services developed can utilize many pedagogical approaches and a variety of models to produce a wide range of highly flexible solutions. This paper identifies benefits to learners brought through adopting the multi-model approach gathered over four years of student evaluation. It briefly describes the evaluation of the Adaptive Personalized eLearning Service (APeLS), a personalized eLearning service based on a generic adaptive engine