TY  - BOOK
AU  - Orr,Dominic
AU  - Luebcke,Maren
AU  - Schmidt,J.Philipp
AU  - Ebner,Markus
AU  - Wannemacher,Klaus
AU  - Ebner,Martin
AU  - Dohmen,Dieter
ED  - SpringerLink (Online service)
TI  - Higher Education Landscape 2030: A Trend Analysis Based on the AHEAD International Horizon Scanning 
T2  - SpringerBriefs in Education,
SN  - 9783030448974
AV  - LB2300-2799.3 
U1  - 378 23
PY  - 2020///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Higher education
KW  - Organization
KW  - Planning
KW  - International education 
KW  - Comparative education
KW  - Higher Education
KW  - International and Comparative Education
N1  - A University Landscape for the Digital World -- From Lines of Development to Scenario Development -- Four Models of Higher Education in 2030 -- Outlook on a New University Landscape in 2030 -- Appendix; Open Access
N2  - This open access Springer Brief provides a systematic analysis of current trends and requirements in the areas of knowledge and competence in the context of the project “(A) Higher Education Digital (AHEAD)—International Horizon Scanning / Trend Analysis on Digital Higher Education.” It examines the latest developments in learning theory, didactics, and digital-education technology in connection with an increasingly digitized higher education landscape. In turn, this analysis forms the basis for envisioning higher education in 2030. Here, four learning pathways are developed to provide a glimpse of higher education in 2030: Tamagotchi, a closed ecosystem that is built around individual students who enter the university soon after secondary education; Jenga, in which universities offer a solid foundation of knowledge to build on in later phases; Lego, where the course of study is not a monolithic unit, but consists of individually combined modules of different sizes; and Transformer, where students have already acquired their own professional identities and life experiences, which they integrate into their studies. In addition, innovative practice cases are presented to illustrate each learning path
UR  - https://doi.org/10.1007/978-3-030-44897-4
ER  -