Modeling of Residual Knowledge Estimation in Smart University

Abstract

Smart university is the element of educational environment of smart education. Smart university forms an intellectual infrastructure that ensures the introduction, use, and development of smart technologies in education. The aim of the study is to develop and test models for estimation scale of residual knowledge level in a smart university. The research indicated that the source data is used to build a model formed of a mathematical apparatus and approaches to its development are based on the identification parameters. Estimation models are a part of analytics learning needed to study, monitor, and predict the dynamics of remaining knowledge. The models, aimed to estimate students’ residual knowledge, can be used by smart universities to (a) build individual educational trajectories, while correcting curricula in preparation for an independent assessment of knowledge, (b) for preparation for the accreditation examination, or (c) in preparation for professional and public examination. The received data can be collected and processed by educational data mining tools.