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PASTUSHENKO, O.; OLIVEIRA, W.; HRUŠKA, T.; ISOTANI, S.
Original Title
A Methodology for Multimodal Learning Analytics and Flow Experience Identification within Gamified Assignments
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
Much research has sought to provide a flow experience for students in gamified educational systems to increase motivation and engagement. However, there is still a lack of quantitative research for evaluating the influence of the flow state on learning outcomes. One of the issues related to flow experience identification is that used techniques are often invasive or not suitable for massive applications. The current paper suggests a way to deal with this challenge. We describe a methodology based on multimodal learning analytics, aimed to provide automatic students flow experience identification in the gamified assignments and measuring its influence on the learning outcomes. The application of the developed methodology showed that there are correlations between learning outcomes and flow state, but they depend on the initial level of the user. This finding suggests adding dynamic difficulty adjustment to the gamified assignment.
English abstract
Keywords
gamification, flow theory, multimodal learning analytics, automatic identification, educational systems
Key words in English
Authors
RIV year
2021
Released
25.04.2020
Publisher
Association for Computing Machinery
Location
Honolulu
ISBN
978-1-4503-6819-3
Book
Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
Pages from
1
Pages to
9
Pages count
URL
https://www.fit.vut.cz/research/publication/12184/
BibTex
@inproceedings{BUT168473, author="PASTUSHENKO, O. and OLIVEIRA, W. and HRUŠKA, T. and ISOTANI, S.", title="A Methodology for Multimodal Learning Analytics and Flow Experience Identification within Gamified Assignments", booktitle="Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems", year="2020", pages="1--9", publisher="Association for Computing Machinery", address="Honolulu", doi="10.1145/3334480.3383060", isbn="978-1-4503-6819-3", url="https://www.fit.vut.cz/research/publication/12184/" }
Documents
chi20e-sub1824-i7