| Full text | |
| Author(s): |
Pastushenko, Olena
;
Oliveira, Wilk
;
Isotani, Seiji
;
Hruska, Tomas
;
Assoc Comp Machinery
Total Authors: 5
|
| Document type: | Journal article |
| Source: | CHI'20: EXTENDED ABSTRACTS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS; v. N/A, p. 9-pg., 2020-01-01. |
| 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 and other flow experience dimension to the gamified assignment. (AU) | |
| FAPESP's process: | 18/07688-1 - Design and automatic detection of flow experience in students and teachers in gamified intelligent educational systems |
| Grantee: | Wilk Oliveira dos Santos |
| Support Opportunities: | Scholarships in Brazil - Doctorate |