Detail publikace

Gamification in Assignments: Using Dynamic Difficulty Adjustment and Learning Analytics to Enhance Education

PASTUSHENKO, O.

Originální název

Gamification in Assignments: Using Dynamic Difficulty Adjustment and Learning Analytics to Enhance Education

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

This paper discusses the opportunities for gamification and dynamic difficulty adjustment based on multimodal learning analytics in assignments. Altogether this covers a broader term of personalized education, which is getting more attention among the researchers in recent years. The difference of this work from other similar researches is that it suggests combining several domains to achieve better results: gamification (in order to improve student's motivation and involvements), and dynamic difficulty adjustment. All this is made possible by applying multimodal learning analytics and creating useful learning dashboards for the teachers.

Klíčová slova

gamification; dynamic difficulty adjustment; personalized learning; multimodal learning analytics

Autoři

PASTUSHENKO, O.

Vydáno

3. 9. 2019

Nakladatel

Association for Computing Machinery

Místo

Barcelona

ISBN

978-3-319-77711-5

Kniha

Extended Abstracts of the Annual Symposium on Computer-Human Interaction in Play Companion

Edice

Extended Abstracts

Strany od

47

Strany do

53

Strany počet

7

URL

https://www.fit.vut.cz/research/publication/12063/

BibTex

@inproceedings{BUT161461,
  author="Olena {Pastushenko}",
  title="Gamification in Assignments: Using Dynamic Difficulty Adjustment and Learning Analytics to Enhance Education",
  booktitle="Extended Abstracts of the Annual Symposium on Computer-Human Interaction in Play Companion",
  year="2019",
  series="Extended Abstracts",
  pages="47--53",
  publisher="Association for Computing Machinery",
  address="Barcelona",
  doi="10.1145/3341215.3356335",
  isbn="978-3-319-77711-5",
  url="https://www.fit.vut.cz/research/publication/12063/"
}

Dokumenty

CHI2019_doctoral_consortium.pdf