Session Information
16 SES 06 A, Digital Games in Education
Paper Session
Contribution
Compared with the one-size-fits-all gamification, tailored gamification highlights the importance of individual differences for learning and motivates students by modifying game elements to match their personal user profiles. Yet, it is a challenge for teachers and curriculum designers to use it in practice, since a limited number of studies in this field currently discuss ‘how to tailor’ in the educational settings. The systematic review examined research on tailored gamification for learning based on 43 peer-reviewed articles published between 2013 and 2023. The study aims to investigate tailored gamification for learning by considering the types of student information for creating user profiles, approaches to tailor, and game elements used when tailoring. The details related to student information, tailored approaches and game elements are depicted in tables. According to the taxonomy of Missaoui and Maalel (2021), student information in gamified contexts were grouped as ‘learner information’ (e.g., learning goal and skill), personal information (e.g., demographic data and personality trait), and player information (e.g., player type and preference). The tailored approaches were categorized as personalization, adaption and recommendation by adopting the taxonomies of Klock, et al. (2020). Then we applied the ways of Toda et al. (2019) to categorize game elements for tailored gamification in education into five types, namely, personal, social, ecological, performance, and fictional game elements.
Apart from student learning, personal, and player information, we found that contextual information students in can also differentiate students and should be included into their user profiles when tailoring gamification. Additionally, tailored approaches in the studies that were reviewed included personalization, adaption, recommendation, with user modeling as their basis. Twenty-three game elements in five categories were employed in tailored gamification when using these types of tailored approaches. These results indicated that, students’ user profiles relied on their player information more often, than on their learning and personal information, one main reason for which was that there existed the most existing typologies to identify students’ player types. Second, only a few articles in this review study integrated different aspects of student information to build user profiles and most of them ignored the complexity of human characteristics and needs. Third, most studies modeled users by exploring the types of student information in their profiles, rather than conducting the tailored gamifying classes. In the real learning contexts, personalization and adaption were more commonly reported than recommendation. Moreover, a variety of game element categories reflect multiple aspects of a tailored gamifying system, and each tailored approach has their own preferred types of game elements, respectively.
Researchers should explore more student information and apply multiple types of them when building user profiles in tailored gamification systems and teachers should consider students’ learning contexts and give them instant scaffolding when using gamified systems. Second, to bridge the gap between preparation and implementation, we suggest future researchers conduct design-based studies to develop and evaluate tailored gamification as part of teachers’ instructional practice. Additionally, experimental designs with non-tailored gamification classes as comparisons might help to examine the student outcomes in a rigorous way. Since all game element clusters are important for enhancing student motivation during gamified classes, we would therefore encourage more empirical research on the impact of using all the game element clusters when tailoring gamification for learning.
These findings provide a holistic picture of how to tailor gamification for learning to motivate students. Teachers and curriculum designers can benefit from this study to consider appropriate student information used in user profiles, and tailored approaches during both the class design and implementation, and select appropriate game elements by understanding their game elements when adopting different tailored approaches.
Method
The methodology is the systematic literature review. The principles of the PRISMA statement (Moher et al., 2009) will be used as a guideline to conduct and report this review work. This literature research is conducted with electronic databases in a research university library in the Netherlands and uses the snowballing method to retrieve relevant literature as necessary supplements. This study aims to examine tailored gamification with the consideration of individual differences in educational settings to expand the current body of knowledge in this area. Based on this research purpose, the keywords for searching consist of the synonyms of tailor (e.g., personalize) and variants for gamification (e.g., gamified) and education (e.g., school, learning, and teaching). Besides, the papers will be included from 2013 onwards because from then, tailored gamification began to be emphasized in educational settings (Klock et al., 2018). The selected papers should be (a) focusing on tailored gamification (e.g., not the general gamified technique or not irrelevant with gamification) (b) written in English (c) records with full access (d) available in full text (e) primary studies (e.g., not surveys or systematic mappings or reviews) (f) peer-reviewed articles (g) in educational settings (h) published from 2013 to date. This period is chosen due to from 2013 onward, tailored gamification began to be studied (Klock et al., 2018) and the scope reaches the year 2023 to collect state-of-the-art research data on this topic. The details related to student information, tailored approaches and game elements are depicted in tables. Based on the findings of the selected articles, each article has been coded by (1) instruments (2) student information types (3) typologies in Table 1. Table 2 displayed the tailored approaches categorized by adopting the taxonomies of Klock, et al. (2020) as user modeling (basis), personalization, adaption, and recommendation. To illustrate the different processes of these approaches, a four-step tailored framework employed by Shute et al. (2012) was used. Each article in Table 2 has been coded by (a) author/year, (b) country, (c) discipline, (d) educational level, (e) tailored approach, (f) capture, (g) analyze, (h) select, (i) present. Among them, the (h) select step related to the game elements was explained separately in Table 3. Then in order to illustrate different functions of game elements used in tailored gamification for learning, we categorized them into five types, namely, personal, social, ecological, performance, and fictional, according to Toda et al. (2019).
Expected Outcomes
For researchers, this study distinguished fifteen types of student information stored in user profiles and twelve data instruments for collecting these information. Students’ user-profile was mostly dependent on their player types, learning behavior and performance in class. Besides, this study categorized three approaches to tailor gamification in education and characterized game elements with various functions used in this area. This review extends the previous focus on the types of tailored approaches for gamified learning such as personalization in Aljabali and Ahmad (2018). Furthermore, what game elements existed and what functions they had in tailored gamification are illustrated in this study, which helps cover the research limitations of Hallifax et al. (2019) and Bennani et al. (2020). Future researchers are suggested to conduct more empirical studies to compare the motivating effect between tailored and non-tailored gamification, and also between personalization, adaption and recommendation approaches. More types of student information need to be considered, especially the contexts they are in, since humans have diverse characteristics. Practical implications are given as well. Teachers should introduce tailored gamification comprehensively along with illustrative examples (e.g., videos of tailored gamification lessons) before their class, because tailored gamification is a new technology and has not been widely adopted for learning. Furthermore, the implementation of three tailored approaches relies heavily on user modeling to create individuals’ user profiles. Therefore, students’ acceptance of collecting their personal data is of great importance for teaching effectiveness. During the class, teachers should pay close attention to students’ behavior and performance and provide scaffolding to them when they encounter problems with the use of gamified systems, to facilitate the smooth running of the tailored process. Apart from students’ human aspects (e.g., player type, learning style), teachers should consider students’ learning contexts, especially for out-of-class learning.
References
Aljabali, R. N., & Ahmad, N. (2018). A review on adopting personalized gamified experience in the learning context. IEEE Conference on e-Learning, e-Management and e-Services, 61-66. Bennani, S., Maalel, A., & Ghezala, H. B. (2020). AGE-Learn: Ontology-based representation of personalized gamification in E-learning. Procedia Computer Science, 176, 1005-1014. Hallifax, S., Serna, A., Marty, J. C., & Lavoué, É. (2019). Adaptive gamification in education: A literature review of current trends and developments. European Conference on Technology Enhanced Learning, 294-307. Klock, A. C. T., Pimenta, M. S., & Gasparini, I. (2018). A systematic mapping of the customization of game elements in gamified systems. Brazilian Symposium on Computer Games and Digital Entertainment, 11-18. Klock, A. C. T., Gasparini, I., Pimenta, M. S., & Hamari, J. (2020). Tailored gamification: A review of literature. International Journal of Human-Computer Studies, 144. Missaoui, S., & Maalel, A. (2021). Student’s profile modeling in an adaptive gamified learning environment. Education and Information Technologies, 26(5), 6367–6381. Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group*. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151(4), 264-269. Shute, V. J., & Zapata-Rivera, D. (2012). Adaptive educational systems. Adaptive technologies for training and education, 7(27), 1-35. Toda, A. M., Klock, A. C., Oliveira, W., Palomino, P. T., Rodrigues, L., Shi, L., Bittencourt, lg., Gasparini, I., Isotani, S., & Cristea, A. I. (2019). Analysing gamification elements in educational environments using an existing Gamification taxonomy. Smart Learning Environments, 6(1), 1-14.
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