Towards Automatic Flow Experience Identification in Educational Systems: A Qualitative Study

• Wilk Oliveira USP https://orcid.org/0000-0003-3928-6520
• Armando M. Toda USP https://orcid.org/0000-0003-2681-8698
• Paula T. Palomino USP https://orcid.org/0000-0002-9730-2253
• Luiz Rodrigues USP https://orcid.org/0000-0003-0343-3701
• Lei Shi Durham University
• Seiji Isotani USP https://orcid.org/0000-0003-1574-0784

DOI: https://doi.org/10.5753/cbie.sbie.2020.702

Resumo

One of the main challenges in the field of learning technologies is the automatic students' flow experience identification in educational systems. This challenge occurs because the flow experience identification is usually conducted by using invasive techniques (e.g., eye trackers or electroencephalograms) or approaches that are not able to handle a large number of students at the same time (e.g., questionnaires/scales or interviews). Despite recent studies proposed to analyze students' flow experience based on user data logs, most of these studies are theoretical without data-based analysis. To move towards the automatic flow experience identification in educational systems, we conducted a qualitative study to analyze possible relations between the students' interaction data logs, behavior, and flow experience in an educational system. The results show that some data logs are related to the students' flow experience. Our results provide insights into the relations amongst the data logs, the flow experience, and students' behavior, which could be beneficial to the development of an approach for automatic identification of the flow experience in educational systems.

Referências

Alhadreti, O. and Mayhew, P. (2016). “thinking about thinking aloud”: An investigation of think-aloud methods in usability testing. InProceedings of the 30th International BCS Human Computer Interaction Conference 30, pages 1–3.


Bai, S., Hew, K. F., and Huang, B. (2020). Is gamification “bullshit”? evidence from a meta-analysis and synthesis of qualitative data in educational contexts. Educational Research Review, page 100322.


Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2):77–101.


Buil, I., Catalán, S., and Martínez, E. (2019). The influence of flow on learning outcomes:An empirical study on the use of clickers.British Journal of Educational Technology,50(1):428–439.


Charters, E. (2003). The use of think-aloud methods in qualitative research an introduction to think-aloud methods. Brock Education: A Journal of Educational Research and Practice, 12(2).


Csikszentmihalyi, M. (1997a). Finding flow: The psychology of engagement with every-day life. Basic Books.


Csikszentmihalyi, M. (1997b). Flow and education. NAMTA journal, 22(2):2–35.


Csikszentmihalyi, M. (2014). Toward a psychology of optimal experience. In Flow and the foundations of positive psychology, pages 209–226. Springer.


Csikszentmihalyi, M. (2020). Finding flow: The psychology of engagement with everydaylife. Hachette UK.


Erhel, S. and Jamet, E. (2019). Improving instructions in educational computer games: Exploring the relations between goal specificity, flow experience and learning outcomes. Computers in Human Behavior, 91:106–114.


Fonteyn, M. E., Kuipers, B., and Grobe, S. J. (1993). A description of think aloud methodand protocol analysis. Qualitative health research, 3(4):430–441.


Hair Jr, J. F., Hult, G. T. M., Ringle, C., and Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.


Hamari, J. and Koivisto, J. (2014). Measuring flow in gamification: Dispositional flow scale-2.Computers in Human Behavior, 40:133–143.


Heutte, J., Fenouillet, F., Martin-Krumm, C., Boniwell, I., and Csikszentmihalyi, M.(2016). Proposal for a conceptual evolution of the flow in education (eduflow) model. In 8th European Conference on Positive Psychology (ECPP 2016).


Jackson, S., Eklund, B., and Martin, A. (2011). The flow manual the manual for the flow scales manual. sampler set. Mind, 2011:1–85.


Jackson, S. A. and Eklund, R. C. (2002). Assessing flow in physical activity: The flow state scale–2 and dispositional flow scale–2. Journal of Sport and Exercise Psychology,24(2):133–150.


Lee, P.-M., Jheng, S.-Y., and Hsiao, T.-C. (2014). Towards automatically detecting whether student is in flow. In International Conference on Intelligent Tutoring Systems, pages 11–18. Springer.


Likert, R. (1932). A technique for the measurement of attitudes.Archives of psychology.


Marinho, A., Oliveira, W., Bittencourt, I. I., and Dermeval, D. (2019). Does gamification improve flow experience in classroom? an analysis of gamer types in collaborative and competitive settings. Brazilian Journal of Computers in Education, 27(02):40.


Oliveira, W., Bittencourt, I. I., Isotani, S., Dermeval, D., Marques, L. B., and Silveira, I. F.(2018). Flow theory to promote learning in educational systems: Is it really relevant? Brazilian Journal of Computers in Education, 26(02):29.


Oliveira, W. and Isotani, S. (2019). Towards to automatic flow experience identification in educational systems: A based human-computer interaction approach. In Proceedingsof the 2019 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts. ACM.


Oliveira, W., Toda, A., Palomino, P., Rodrigues, L., Shi, L., and Isotani, S. (2019).Towards automatic flow experience identification in educational systems: A theory-driven approach. In Proceedings of the 2019 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts. ACM.


Oliveira, W., Toda, A., Toledo, P., Shi, L., Vassileva, J., Bittencourt, I. I., and Isotani, S. (2020). Does tailoring gamified educational systems matter? the impact on students’ flow experience. In Proceedings of the 53rd Hawaii International Conferenceon System Sciences.


Orji, R., Mandryk, R. L., and Vassileva, J. (2014). Selecting effective strategies for tailoring persuasive health games to gamer types.GRAND.


Pastushenko, O., Oliveira, W., Isotani, S., and Hruška, T. (2020). A methodology for multimodal learning analytics and flow experience identification within gamified assignments. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pages 1–6.


Riessman, C. K. (1993).Narrative analysis, volume 30. Sage.


Shi, L. and Cristea, A. I. (2016). Motivational gamification strategies rooted in self-determination theory for social adaptive e-learning. In Micarelli, A., Stamper, J., and Panourgia, K., editors, Intelligent Tutoring Systems, pages 294–300, Cham. Springer International Publishing.


renor, J. M., Miller, M. K., and Gipson, K. G. (2011). Utilization of a think-aloud protocol to cognitively validate a survey instrument identifying social capital resources of engineering undergraduates. In American Society for Engineering Education. American Society for Engineering Education.