Game-based Activity Design in Primary School Students’ Learning Style Detection

Abstract

Generation Alpha, the present primary school cohort born after 2010, has significant exposure to mobile devices and gaming. Adopting a "One Size Fits All" approach in modern teaching methods may not be effective, as it overlooks individual learning preferences. Personalized learning can be facilitated by identifying a student’s learning style (LS). Adaptive learning based on LS has been found to have positive effects in several studies. However, traditional learning style detection techniques such as questionnaires and self-assessments can be time-consuming and demotivating for primary school students. This study aims to propose a game-based activity framework as an alternative to the Index of Learning Style (ILS) questionnaire linked with Felder Silverman Learning Style Model for LS detection. The proposed game was evaluated with a sample of sixty students, and preliminary results indicate that the game outperforms the original ILS questionnaire in terms of student engagement and motivation to complete LS activities, achieving an overall satisfaction rate of 87.5%. The second phase of the research will focus on evaluating the accuracy of LS prediction using the designed game, which is currently ongoing.