Integrated Learning Pathways in Higher Education: A Framework Enhanced with Machine Learning and Semantics

Autor/inn/en	Iatrellis, Omiros; Savvas, Ilias K.; Kameas, Achilles; Fitsilis, Panos
Titel	Integrated Learning Pathways in Higher Education: A Framework Enhanced with Machine Learning and Semantics
Quelle	In: Education and Information Technologies, 25 (2020) 4, S.3109-3129 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Iatrellis, Omiros)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-020-10105-7
Schlagwörter	Higher Education; Learning Processes; Electronic Learning; Semantics; Foreign Countries; Computer Science Education; Educational Quality; Quality Assurance; Computer Interfaces; Technology Integration; Greece + Suchen Sie Ihr Suchwort? Hochschulbildung; Hochschulsystem; Hochschulwesen; Learning process; Lernprozess; Semantik; Ausland; Computer science lessons; Informatikunterricht; Quality of education; Bildungsqualität; Qualitätssicherung; Griechenland
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01