No full text
Unpublished conference/Abstract (Scientific congresses, symposiums and conference proceedings)
Contrasting classical and machine learning approaches in the estimation of value-added scores in large-scale educational data.
Levy, Jessica; Mussack, Dominic; Brunner, Martin et al.
20208. Tagung der Gesellschaft für Empirische Bildungsforschung (GEBF2020)
 

Files


Full Text
No document available.

Send to



Details



Disciplines :
Education & instruction
Author, co-author :
Levy, Jessica ;  University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Luxembourg Centre for Educational Testing (LUCET)
Mussack, Dominic ;  University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Education, Culture, Cognition and Society (ECCS)
Brunner, Martin
Keller, Ulrich ;  University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Luxembourg Centre for Educational Testing (LUCET)
Cardoso-Leite, Pedro ;  University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Education, Culture, Cognition and Society (ECCS)
Fischbach, Antoine  ;  University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Luxembourg Centre for Educational Testing (LUCET)
External co-authors :
yes
Language :
English
Title :
Contrasting classical and machine learning approaches in the estimation of value-added scores in large-scale educational data.
Publication date :
March 2020
Event name :
8. Tagung der Gesellschaft für Empirische Bildungsforschung (GEBF2020)
Event organizer :
University of Potsdam
Event place :
Potsdam, Germany
Event date :
from 25-03-2020 to 27-03-2020
Audience :
International
FnR Project :
FNR10921377 - Capitalising On Linguistic Diversity In Education, 2015 (15/01/2017-14/07/2023) - Peter Gilles
Commentary :
THE CONFERENCE WAS CANCELLED DUE TO THE CORONAVIRUS OUTBREAK (COVID-19)
Available on ORBilu :
since 13 January 2020

Statistics


Number of views
95 (22 by Unilu)
Number of downloads
0 (0 by Unilu)

Bibliography


Similar publications



Contact ORBilu