Student Player Types in Higher Education—Trial and Clustering Analyses

Abstract

In the context of the ongoing transformation in education, new learning methods, as well as new technologies, and therefore new forms of interactions are challenging higher education. This challenge can be addressed through ambient learning management systems that adapt to the student in the presentation and preparation of course materials. For educational games offered in such systems, this means that the game mechanics should be adapted to the student. To narrow down the sum of mechanics to the amount that is relevant for students, player types can be identified. This paper investigates the player types among students at the University of Lübeck. The characteristics of all player types of Marczewski’s Gamification User Types Hexad Framework are considered using a clustering method for the analysis. The result is three profiles with different characteristics of player types. For each of the profiles, mechanics are suggested which can be used for the respective profile. Thus, educational games can be more easily and automatically adapted to player type.

Navigating the Evolution of Game-Based Educational Approaches in Secondary STEM Education: A Decade of Innovations and Challenges

“…In addition, recent research emphasized the importance of tailoring game mechanics to individual types of players. For example, a study conducted at the University of Lübeck identified three player profiles using Marczewski's Hexad Framework [57] and recommended specific mechanics for each profile to improve the effectiveness of educational games [58]. Despite the open debate about potential taxonomies, considering player profiles in the design of educational games can improve their effectiveness, foster deeper understanding, and support interest and engagement in STEM education [59].…”

“…As delineated in the "Methodology" section, the employment of quantitative textual analysis of titles and abstracts from academic publications over a specified period facilitated the identification and examination of trends and patterns within the academic landscape, thereby fostering a comprehensive grasp of the subject matter. Indeed, co-word analysis proved exceptionally adept at capturing the dynamic nature of research trends [57,58]. Moreover, this method enabled a more profound comprehension of the interrelationships among various research themes, offering a detailed depiction of the evolution of GBL in STEM education for secondary schools during the observed timeframe.…”