Integrated learning pathways in higher education: A framework enhanced with machine learning and semantics

Abstract

The present research work proposes the development of an integrated framework for the personalization and parameterization of learning pathways, aiming at optimizing the quality of the offered services by the Higher Educational Institutions (HEI). In order to achieve this goal, in addition to the educational part, the EDUC8 framework encloses the set of parameters that cover both the technical and the financial dimensions of a learning pathway, thus providing a complete tool for the optimization and calculation of the offered services by the HEIs in combination with the minimization of respective costs. Moreover, the proposed framework incorporates simulation modeling along with machine learning for the purpose of designing learning pathways and evaluating quality assurance indicators and the return on investment of implementation. The study presents a case study in relation to tertiary education in Greece, with a particular focus on Computer Science programs. Data clustering is specifically applied to learn potential insights pertaining to student characteristics, education factors and outcomes. Generally, the framework is conceived to provide a systematic approach for developing tertiary policies that help optimize the quality and cost of education. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.