Exploratory Study of the Relationship Between HEXAD Player Types and Cognitive Learning Objectives

Abstract

Introduction and Aim Despite the widespread practical application of gamification in education, existing empirical evidence of its effectiveness often lacks a theoretical foundation. In particular, there is a lack of systematic analysis of specific learning outcomes in relation to cognitive complexity, without taking modern taxonomies into account. The aim of this study is to identify initial trends regarding behavior in a gamified learning environment and to explore the extent to which there is a systematic relationship between the individual gamification player types of the HEXAD model and the attainment of different cognitive learning levels according to the taxonomy of Anderson and Krathwohl. Methods The study employs a mixed-methods approach (explanatory sequential design). In the first quantitative phase (n = 12), participants complete a gamified online test via LimeSurvey. The instructional design is based on the taxonomy developed by Anderson and Krathwohl, while the gamification draws on insights from Krath and von Korflesch. The data is analyzed using descriptive and exploratory statistics (IBM SPSS). Specific research questions are derived from these results to provide a deeper explanation of the quantitative findings for the second qualitative phase of guided interviews (n = 7; analyzed via Atlas.ti). Finally, the quantitative and qualitative data are triangulated in a joint display. Results The analysis reveals differences in learning outcomes depending on player type and cognitive complexity. Players and Achievers perform better on tangible learning objectives. However, the performance curve for Players declines as cognitive complexity increases. Philanthropists, on the other hand, demonstrate their strength in analytical, subjective, and evaluative tasks and show a continuous upward trend in meaningful, content-rich challenges. Socialisers exhibit consistent performance on tasks of moderate complexity, but a drop in performance when it comes to evaluation. Additionally, a crossover effect from Players to Philanthropists can be observed when it comes to evaluative goals. Conclusion The results suggest a task-specific relationship between individual characteristics of the HEXAD player types and learning success at various cognitive levels. It can therefore be concluded that future game-based learning applications should be explicitly tailored to the specific game design elements and cognitive requirements of the respective player types. To verify these exploratory findings, it is recommended that follow-up studies be conducted with a larger sample size over a longer observation period.