Data-driven analyses of electronic text books

Research output: Contributions to collected editions/worksContributions to collected editions/anthologiesResearch

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 AG, 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/worksContributions to collected editions/anthologiesResearch

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 AG, 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 AG. 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 AG. 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 AG",
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 AG

CY - Cham

ER -