Stephanie Heintz’s research while affiliated with University of Leicester and other places

Our main research question is how the choice of game type influences the success of digital educational games (DEGs), where success is defined as significant domain-specific knowledge gain (learning outcome) with positive player experience. We propose a methodological framework to address this question. The comparison of different game types is based on the previously developed Game Elements-Attributes Model (GEAM) and the Game Genre Map, which summarise game features and their relations. In addition, we present a research model considering the impact of player characteristics on learning outcome and player experience as well as their interrelation. Two empirical studies were conducted with 280 students. The application domain was computer programming. Study 1 compared three DEGs of the Mini-Game genre, differing in a single GEAM attribute—time pressure vs. puzzle solving and abstract vs. realistic settings. Study 2 compared DEGs of different genres, which vary in the implementation of several GEAM attributes. None of the player characteristics were found to be statistically significant factors. For both studies, significant differences were found in learning outcomes, for Study 2 also in some of the player experience dimensions. GEAM was demonstrated as a promising framework for games user research.

The existing common video game genres lack clarity as well as consistency and thus cannot serve as a solid reference to inform the research on digital educational games (DEG), which are increasingly used as learning tools. To address this basic issue of game classification, we have developed a web-based survey to collect data on how people play and perceive video games that they know well. The survey is grounded in our Game Elements-Attributes Model (GEAM). 321 valid responses were analysed using established hierarchical clustering methods and a novel mapping technique that computes the degree of relevance of individual game attributes to game types and visualises them with hues of grey. The game genre map so obtained can improve the existing game classification.

Research on the comparison of different design options and implementation choices for digital educational games (DEGs) is lacking, despite the possibly crucial impact of these options on the learning outcome. Although game features with the potential to support learning have been identified and reported in the literature, an underlying comprehensive game model, providing structure for a comparison study and ensuring completeness in covering of all relevant features, is needed. To address this issue a literature search for game models was conducted and the resulting models were analysed for their applicability in this case. Several limitations and shortcomings of the existing models drove us to develop the Game Elements-Attributes Model (GEAM) by consolidating game definitions and models identified from an extensive literature review. GEAM can serve as a foundation for the comparison of DEGs or digital games in general, but may also be valuable for other areas of game studies.

Digital educational games (DEGs) are increasingly recognized as a promising tool for learning. To deepen the understanding of how two key components - content and player - of DEG contribute to learning effects, we developed a model on game elements. It informed the creation of a mini-game on programming, which was evaluated with 50 computer science undergraduates as a pilot study. Data were collected with questionnaires on background, domain-specific knowledge as well as user perception and with a screen cast recorder. Results show that most of the design requirements derived from the model were met. Pre-knowledge was found to be a significant factor influencing user perception. Implications to future work on implementing design elements such as feedback are drawn.