Gamification and learning performance : a systematic review of the literature

Over the last 10 years, research on gamification, the use of game elements in non-game contexts, has increased in the field of education, due to its potential to enhance learning performance. Yet, the majority of available research rather focuses on the evaluation of motivation and engagement as key dependent variables. Hence, the purpose of this study is to review available studies on gamification, with an exclusive focus on learning performance as the key dependent variable. Through a systematic search and selection process, building on "Web of Science" articles and by considering studies between 2000 and 2016 related to gamification and learning, 582 articles were identified. Further inclusion and exclusion criteria, regarding setting (education), study focus (empirical), journal access (full access) and dependent variables (learning performance), resulted in a review of 23 articles meeting the criteria. The analysis of these articles showed how gamification could be linked to a direct increase in learning performance of students. Nevertheless, some studies also reflect weaker statistical differences between being involved or not in a gamified environment. The review analysis results are especially helpful to define a future agenda for gamification research, addressing the following gaps in the literature. First, include mediating and moderating variables to find more empirical research that can prove an indirect effect of gamification on learning performance. Second, carry out additional research that empirically underpins the direct linkage between gamification and learning performance. Third, include specific individual gamification elements to be able to determine explicit differential effects of these elements on learning performance. Fourth, conduct research in a broader range of knowledge fields to develop empirical evidence in the context of other knowledge domains next to computer sciences. Finally, consider involving larger sample and setting up longer experimental interventions, to avoid novelty effects and risks of lack of generalization