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Generalising IRT to Discriminate Between Examinees. /
Boubekki, Ahcène; Brefeld, Ulf; Delacroix, Thomas.
Proceedings of the International Conference on Educational Data Mining. ed. / Olga Cristina Santos; Jesus Gonzalez Boticario; Cristobal Romeo; Mykola Pechenizkiy; Agathe Merceron; Piotr Mitros; Jose Maria Luna; Cristian Mihaescu; Pablo Moreno; Arnon Hershkovitz; Sebastian Ventura; Michel Desmarais. Madrid: National University for Distance Education (UNED), 2015. p. 604-605.
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Boubekki, A, Brefeld, U & Delacroix, T 2015,
Generalising IRT to Discriminate Between Examinees. in OC Santos, JG Boticario, C Romeo, M Pechenizkiy, A Merceron, P Mitros, JM Luna, C Mihaescu, P Moreno, A Hershkovitz, S Ventura & M Desmarais (eds),
Proceedings of the International Conference on Educational Data Mining. National University for Distance Education (UNED), Madrid, pp. 604-605, 8th International Conference on Educational Data Mining - EDM 2015 , Madrid, Spain,
26.06.15. <
http://www.educationaldatamining.org/EDM2015/proceedings/edm2015_proceedings.pdf>
APA
Boubekki, A., Brefeld, U., & Delacroix, T. (2015).
Generalising IRT to Discriminate Between Examinees. In O. C. Santos, J. G. Boticario, C. Romeo, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, & M. Desmarais (Eds.),
Proceedings of the International Conference on Educational Data Mining (pp. 604-605). National University for Distance Education (UNED).
http://www.educationaldatamining.org/EDM2015/proceedings/edm2015_proceedings.pdf
Vancouver
Boubekki A, Brefeld U, Delacroix T.
Generalising IRT to Discriminate Between Examinees. In Santos OC, Boticario JG, Romeo C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, Desmarais M, editors, Proceedings of the International Conference on Educational Data Mining. Madrid: National University for Distance Education (UNED). 2015. p. 604-605
Bibtex
@inbook{0171c5d2f9ef47518069e608eb8f9fd0,
title = "Generalising IRT to Discriminate Between Examinees",
abstract = "We present a generalisation of the IRT framework that allows to discriminate between examinees. Our model therefore introduces examinee parameters that can be optimised with Expectation Maximisation-like algorithms. We provideempirical results on PISA data showing that our approach leads to a more appropriate grouping of PISA countries than by test scores and socio-economic indicators.",
keywords = "Informatics, Business informatics",
author = "Ahc{\`e}ne Boubekki and Ulf Brefeld and Thomas Delacroix",
year = "2015",
language = "English",
isbn = "978-84-606-9425-0",
pages = "604--605",
editor = "Santos, {Olga Cristina} and Boticario, {Jesus Gonzalez} and Cristobal Romeo and Mykola Pechenizkiy and Agathe Merceron and Piotr Mitros and Luna, {Jose Maria} and Cristian Mihaescu and Pablo Moreno and Arnon Hershkovitz and Sebastian Ventura and Michel Desmarais",
booktitle = "Proceedings of the International Conference on Educational Data Mining",
publisher = "National University for Distance Education (UNED)",
address = "Spain",
note = "8th International Conference on Educational Data Mining - EDM 2015 , EDM Conference 2015 ; Conference date: 26-06-2015 Through 29-06-2015",
url = "http://educationaldatamining.org/EDM2015/",
}
RIS
TY - CHAP
T1 - Generalising IRT to Discriminate Between Examinees
AU - Boubekki, Ahcène
AU - Brefeld, Ulf
AU - Delacroix, Thomas
N1 - Conference code: 8
PY - 2015
Y1 - 2015
N2 - We present a generalisation of the IRT framework that allows to discriminate between examinees. Our model therefore introduces examinee parameters that can be optimised with Expectation Maximisation-like algorithms. We provideempirical results on PISA data showing that our approach leads to a more appropriate grouping of PISA countries than by test scores and socio-economic indicators.
AB - We present a generalisation of the IRT framework that allows to discriminate between examinees. Our model therefore introduces examinee parameters that can be optimised with Expectation Maximisation-like algorithms. We provideempirical results on PISA data showing that our approach leads to a more appropriate grouping of PISA countries than by test scores and socio-economic indicators.
KW - Informatics
KW - Business informatics
M3 - Article in conference proceedings
SN - 978-84-606-9425-0
SP - 604
EP - 605
BT - Proceedings of the International Conference on Educational Data Mining
A2 - Santos, Olga Cristina
A2 - Boticario, Jesus Gonzalez
A2 - Romeo, Cristobal
A2 - Pechenizkiy, Mykola
A2 - Merceron, Agathe
A2 - Mitros, Piotr
A2 - Luna, Jose Maria
A2 - Mihaescu, Cristian
A2 - Moreno, Pablo
A2 - Hershkovitz, Arnon
A2 - Ventura, Sebastian
A2 - Desmarais, Michel
PB - National University for Distance Education (UNED)
CY - Madrid
T2 - 8th International Conference on Educational Data Mining - EDM 2015
Y2 - 26 June 2015 through 29 June 2015
ER -
