Standard
Data-driven analyses of electronic text books. /
Boubekki, Ahcène; Kröhne, Ulf; Goldhammer, Frank et al.
Solving large scale learning tasks: Challenges and algorithms : essays dedicated to Katharina Morik on the occasion of her 60th birthday. ed. / Stefan Michaelis; Nico Piatkowski; Marco Stolpe. Cham: Springer International Publishing, 2016. p. 362-376 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9580).
Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research
Harvard
Boubekki, A, Kröhne, U, Goldhammer, F, Schreiber, W
& Brefeld, U 2016,
Data-driven analyses of electronic text books. in S Michaelis, N Piatkowski & M Stolpe (eds),
Solving large scale learning tasks: Challenges and algorithms : essays dedicated to Katharina Morik on the occasion of her 60th birthday. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9580, Springer International Publishing, Cham, pp. 362-376.
https://doi.org/10.1007/978-3-319-41706-6_20
APA
Boubekki, A., Kröhne, U., Goldhammer, F., Schreiber, W.
, & Brefeld, U. (2016).
Data-driven analyses of electronic text books. In S. Michaelis, N. Piatkowski, & M. Stolpe (Eds.),
Solving large scale learning tasks: Challenges and algorithms : essays dedicated to Katharina Morik on the occasion of her 60th birthday (pp. 362-376). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9580). Springer International Publishing.
https://doi.org/10.1007/978-3-319-41706-6_20
Vancouver
Boubekki A, Kröhne U, Goldhammer F, Schreiber W
, Brefeld U.
Data-driven analyses of electronic text books. In Michaelis S, Piatkowski N, Stolpe M, editors, Solving large scale learning tasks: Challenges and algorithms : essays dedicated to Katharina Morik on the occasion of her 60th birthday. Cham: Springer International Publishing. 2016. p. 362-376. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-41706-6_20
Bibtex
@inbook{3b21365d8e764000825392f40845f940,
title = "Data-driven analyses of electronic text books",
abstract = "We present data-driven log file analyses of an electronic text book for history called the mBook to support teachers in preparing lessons for their students. We represent user sessions as contextualised Markov processes of user sessions and propose a probabilistic clustering using expectation maximisation to detect groups of similar (i) sessions and (ii) users. We compare our approach to a standard K-means clustering and report on findings that may have a direct impact on preparing and revising lessons.",
keywords = "Business informatics",
author = "Ahc{\`e}ne Boubekki and Ulf Kr{\"o}hne and Frank Goldhammer and Waltraud Schreiber and Ulf Brefeld",
year = "2016",
doi = "10.1007/978-3-319-41706-6_20",
language = "English",
isbn = "978-3-319-41705-9",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer International Publishing",
pages = "362--376",
editor = "Stefan Michaelis and Piatkowski, {Nico } and Marco Stolpe",
booktitle = "Solving large scale learning tasks",
address = "Switzerland",
}
RIS
TY - CHAP
T1 - Data-driven analyses of electronic text books
AU - Boubekki, Ahcène
AU - Kröhne, Ulf
AU - Goldhammer, Frank
AU - Schreiber, Waltraud
AU - Brefeld, Ulf
PY - 2016
Y1 - 2016
N2 - We present data-driven log file analyses of an electronic text book for history called the mBook to support teachers in preparing lessons for their students. We represent user sessions as contextualised Markov processes of user sessions and propose a probabilistic clustering using expectation maximisation to detect groups of similar (i) sessions and (ii) users. We compare our approach to a standard K-means clustering and report on findings that may have a direct impact on preparing and revising lessons.
AB - We present data-driven log file analyses of an electronic text book for history called the mBook to support teachers in preparing lessons for their students. We represent user sessions as contextualised Markov processes of user sessions and propose a probabilistic clustering using expectation maximisation to detect groups of similar (i) sessions and (ii) users. We compare our approach to a standard K-means clustering and report on findings that may have a direct impact on preparing and revising lessons.
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=84978877380&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-41706-6_20
DO - 10.1007/978-3-319-41706-6_20
M3 - Contributions to collected editions/anthologies
SN - 978-3-319-41705-9
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 362
EP - 376
BT - Solving large scale learning tasks
A2 - Michaelis, Stefan
A2 - Piatkowski, Nico
A2 - Stolpe, Marco
PB - Springer International Publishing
CY - Cham
ER -
