Smart learning analytics: Conceptual modeling and agile engineering

Abstract

Learning analytics focuses on collecting, cleaning, processing, visualization and analyzing teaching and learning related data or metrics from a variety of academic sources. Our vision for engineering of smart learning analytics – the next generation of systems and tools for learning analytics - is based on the concept that this technology should strongly support “smartness” levels of smart academic institutions such as adaptivity, sensing, inferring, anticipation, self-learning, and self-organization. This paper presents the up-to-date findings and outcomes of research, design and development project at the InterLabs Research Institute at Bradley University (Peoria, IL, U.S.A.) that is focused on conceptual modeling of smart learning analytics systems, including identification of goals, objectives, features and functions, main components, inputs and outputs, hierarchical and smartness levels, mathematical methods and algorithms for those systems. Agile software engineering approach has been used for a development of a series of software prototypes to verify the design and development process and validate the obtained outcomes for smart learning analytics systems.