Innovative InterLabs System for Smart Learning Analytics in Engineering Education

摘要

Learning analytics is a fast growing area in academia; it is focused on collecting, analysis, cleaning, processing, and visualization of data from various academic sources that are related to course, student, faculty, program director and chair/department levels. This paper presents the features and functions, architectural models, software engineering diagrams, main components, inputs and outputs, hierarchical levels and smartness features, mathematical methods and algorithms used in a design, development, testing and implementation of the innovative InterLabs smart learning analytics system into the Computer Science and Information Systems curriculum at Bradley University (Peoria, IL, USA). Conceptually the InterLabs system is based on (1) smartness features such as adaptivity, sensing, inferring, anticipation, self-learning, self-organization, and (2) the Gartner's Analytics Ascendancy Model. The InterLabs system is focused on descriptive, diagnostic, predictive and prescriptive types of analytics of student academic performance.