Learning analytics challenges: trade-offs, methodology,
scalability

D 2020

Learning analytics challenges: trade-offs, methodology, scalability

PELÁNEK, Radek

Základní údaje

Originální název

Learning analytics challenges: trade-offs, methodology, scalability

Autoři

PELÁNEK, Radek (203 Česká republika, garant, domácí)

Vydání

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

Nakladatel

Association for Computing Machinery

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/20:00118360

Organizační jednotka

Fakulta informatiky

ISBN

978-1-4503-7712-6

DOI

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

UT WoS

000558753800069

Klíčová slova anglicky

trade-offs; scalability; methodology

Štítky

firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 9. 2021 07:56, doc. Mgr. Radek Pelánek, Ph.D.

Anotace

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, s. 554-558. ISBN 978-1-4503-7712-6. Dostupné z: 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, s.~554-558. ISBN~978-1-4503-7712-6. Dostupné z: https://dx.doi.org/10.1145/3375462.3375463.

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