A Web-Based Evaluation Framework for Supporting Novice and Expert Evaluators of Adaptive E- Learning Systems

Based upon the analysed results of an evidence based study conducted over a period of four years, it is clear that it is difficult to identify, for a given evaluation objective, the range of appropriate evaluation techniques (methods, metrics, criteria and approaches to be used). This paper presents a Web-based evaluation framework which is freely available online, designed to support both novice and expert evaluators of adaptive eLearning systems when conducting evaluations. In order to do so we use a recommendation process to identify and recommend the appropriate evaluation techniques. The framework is populated using data extracted from 340 peer reviewed papers containing the evaluation details of adaptive systems. The dataset has the ability to grow over time as the framework itself provides a mechanism for published authors to add their own evaluations to the dataset; thus we believe the dataset is, and will continue to be, very valuable for supporting the design of evaluations of adaptive systems. Keywords—Novice Evaluators, Evaluation Framework, Hybrid Recommender System, Personalised Search, Taxonomy, Webcrawler, Slicer