Abstract:

The problem of smooth and synchronous integration of various types of e-learning and online courses, internships and extra-curricular activities into blended, systematic adaptive educational environment is becoming a new paradigm in innovations in educational process of higher education institutions (HEIs). But there are few studies that consistently discuss the impact of the systems providing analysis of educational outcomes (academic analytics systems) on quality of education and provision of integrated educational environment. This paper investigates role, application and taxonomy of academic analytics systems applied to the process of implementing innovative strategies in Higher Education. Such systems are intended to extract meaningful information from multiple data sources (learning management and content systems (LMS and LCS), online courses and other) to predict, cluster, find relations, and prepare data for decision making process aimed at creating knowledge crucial for improving the quality of educational process in HEIs. Academic analytics incorporate three main types of systems: Learning Analytics (LA), Educational Data Mining (EDM) and Educational Big Data (EBD) systems. As academic analytics research field is rapidly developing even though a considerable number of papers have provided substantial insight on the theoretical basis of LA, EDM and EBD systems, taxonomy, classification and differentiation of types of the systems are still undefined and often overlap. The main research question of the study is to examine the classification of academic analytical systems in higher education according to the needs and goals of universities and investigate impact of academic analytics in blending different types of educational activity and creation of integrated educational environment. Our findings are based on the analysis of papers on learning analytics in higher education published between 2010 and 2018.