Learning analytics challenges: trade-offs, methodology,
scalability

D 2020

Learning analytics challenges: trade-offs, methodology, scalability

PELÁNEK, Radek

Basic information

Original name

Learning analytics challenges: trade-offs, methodology, scalability

Authors

PELÁNEK, Radek (203 Czech Republic, guarantor, belonging to the institution)

Edition

New York, NY, United States, Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, p. 554-558, 5 pp. 2020

Publisher

Association for Computing Machinery

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/20:00118360

Organization unit

Faculty of Informatics

ISBN

978-1-4503-7712-6

DOI

http://dx.doi.org/10.1145/3375462.3375463

UT WoS

000558753800069

Keywords in English

trade-offs; scalability; methodology

Abstract

V originále

Ryan Baker presented in a LAK 2019 keynote a list of six grand challenges for learning analytics research. The challenges are specified as problems with clearly defined success criteria. Education is, however, a domain full of ill-defined problems. I argue that learning analytics research should reflect this nature of the education domain and focus on less clearly defined, but practically essential issues. As an illustration, I discuss three important challenges of this type: addressing inherent trade-offs in learning environments, the clarification of methodological issues, and the scalability of system development.

Citovat

PELÁNEK, Radek. Learning analytics challenges: trade-offs, methodology, scalability. Online. In Christoph Rensing, Hendrik Drachsler. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. New York, NY, United States: Association for Computing Machinery, 2020, p. 554-558. ISBN 978-1-4503-7712-6. Available from: https://dx.doi.org/10.1145/3375462.3375463.

@inproceedings{1750939,
   author = {Pelánek, Radek},
   address = {New York, NY, United States},
   booktitle = {Proceedings of the Tenth International Conference on Learning Analytics & Knowledge},
   doi = {http://dx.doi.org/10.1145/3375462.3375463},
   editor = {Christoph Rensing, Hendrik Drachsler},
   keywords = {trade-offs; scalability; methodology},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York, NY, United States},
   isbn = {978-1-4503-7712-6},
   pages = {554-558},
   publisher = {Association for Computing Machinery},
   title = {Learning analytics challenges: trade-offs, methodology, scalability},
   url = {https://dl.acm.org/doi/10.1145/3375462.3375463},
   year = {2020}
}

TY  - JOUR
ID  - 1750939
AU  - Pelánek, Radek
PY  - 2020
TI  - Learning analytics challenges: trade-offs, methodology, scalability
PB  - Association for Computing Machinery
CY  - New York, NY, United States
SN  - 9781450377126
KW  - trade-offs
KW  - scalability
KW  - methodology
UR  - https://dl.acm.org/doi/10.1145/3375462.3375463
N2  - Ryan Baker presented in a LAK 2019 keynote a list of six grand challenges for learning analytics research. The challenges are specified as problems with clearly defined success criteria. Education is, however, a domain full of ill-defined problems. I argue that learning analytics research should reflect this nature of the education domain and focus on less clearly defined, but practically essential issues. As an illustration, I discuss three important challenges of this type: addressing inherent trade-offs in learning environments, the clarification of methodological issues, and the scalability of system development.
ER  -

PELÁNEK, Radek. Learning analytics challenges: trade-offs, methodology, scalability. Online. In Christoph Rensing, Hendrik Drachsler. \textit{Proceedings of the Tenth International Conference on Learning Analytics \&{} Knowledge}. New York, NY, United States: Association for Computing Machinery, 2020, p.~554-558. ISBN~978-1-4503-7712-6. Available from: https://dx.doi.org/10.1145/3375462.3375463.

Detailed Information on Publication Record