Raising teachers empowerment in gamification design of adaptive learning systems: A qualitative research

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Abstract

Despite the positive outcomes obtained through the application of gamification in the technology-enhanced learning context, previous studies have also reported unexpected results concerning students’ engagement, learning outcomes, and motivation in gamified learning systems. To increase the chances of obtaining positive results in this context, this article proposes a “gamification analytics model for teachers”. In this model, teachers are allowed to define interaction goals, monitor students’ interaction with the system’ learning resources and the gamification elements, and adapt the gamification design through missions to motivate disengaged students to achieve the interaction goals defined. However, the gamification analytics model-based design concepts that will be implemented to support the learning process should be well-planned to teachers’ needs. Hence, one of the contributions of this paper is the validation of twenty design concepts based on the gamification analytics model for teachers by using the speed dating method. Our results suggest that teachers judged useful/relevant visualize students’ interaction with gamification elements such as missions, levels to help them understand the students’ status, but did not evaluate the visualization of the interaction of students with trophies relevant. Teachers also highly evaluated the creation of personalized missions for a student or a specific group as relevant to help demotivated students to engage and achieve the desired goals. Therefore, this study provides some relevant insights to guide the design and re-design of gamified adaptive learning systems.

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Tenório, K., Dermeval, D., Monteiro, M., Peixoto, A., & Pedro, A. (2020). Raising teachers empowerment in gamification design of adaptive learning systems: A qualitative research. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12163 LNAI, pp. 524–536). Springer. https://doi.org/10.1007/978-3-030-52237-7_42

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