An Enhanced xAPI Data Model Supporting Assessment Analytics

Abstract In the learning analytics field, it is highly significant to track and collect the big educational data to improve the learning experience. In fact, one of the e-learning standards for data interoperability which had attracted a remarkable amount of attention in the last years is the Experience API (xAPI). In this paper, we explore the use of xAPI in the learning analytics field. Therefore assessment data represents an important proportion of the educational data generated. When we focus on assessment, we can launch a new source of data that can be analyzed and hence contribute to the improvement of the field of learning analytics. In fact, we discuss the suitability of xAPI standard to track the assessment data and try to enhance its data model to support effectively the assessment analytics. An ontological model is proposed supporting assessment analytics purpose based on the weaknesses of the xAPI data model from assessment point of view. Since our proposed pattern is an ontological model, this gives us the chance to reason about the assessment data by performing some logic rules edited with SWRL(Semantic Web Rule Language) for supporting inference mechanisms related to the leaner level according to its assessment performance.