Reportable Serious Game Mechanics (SGMX): an ontological framework for the analysis and design of digital game-based learning

As digital innovation continues to evolve, providing easier access to game authoring tools and advanced computational technologies, the gap between what is possible and what is practised in digital game-based learning (DGBL) is widening. Current initiatives for competency-based training often employ gamification, which focuses on aesthetics and novelty to enhance engagement at the expense of evidence-based insights. Where data is produced, it typically offers insufficient insights that improve learning design. This study applied a Design-Based Research (DBR) approach, systematically executed through iterative phases, intended to establish a robust framework for DGBL design. Consequently, the study has produced an ontological framework for application at the intersection of learning and game design. 

The initial phase involved a review of the practice of learning design to inform the foundation of the research, which shaped three subsequent iterative stages of primary data collection. Following the DBR methodology, each stage was designed to progressively refine the research context and distil the core research questions, seeking a DGBL design model that can assist learning designers (LDs) in designing and improving organisational training. A survey aimed at LDs and game developers was broadly disseminated. From the respondents, a pool of industry experts was invited to participate in a series of online workshops to discuss and evaluate constructs for identifying causative aspects of DGBL, as presented in the Learning Mechanics – Game Mechanics (LM-GM) framework (Arnab et al., 2015). LM-GM provides a mapping structure that links gameplay with learning processes for the analysis and design of serious games (SGs). A Qualitative Descriptive Research (QDR) approach analysed the resulting conversational data concerning the context for applying LM-GM. The analysis shows a need for a framework integrating human-readable data statements and causality in evidence-based learning to track meaningful learning experiences using evolving technologies.

 The proposed ontological framework, termed Reportable Serious Game Mechanics (SGMX), built on a refined iteration of the LM-GM framework, and designed to generate informative learning data, leverages the principle of reporting meaningful statements by including an actor, a mechanic, and an object. SGMX facilitates the elicitation of grammatically and syntactically consistent game and learning statements, the elements of which can be tracked and reported. These statements are more likely to be human-readable, machine-processable and, importantly, conform to the Experience API (xAPI) standard for rich, informative analytics. 

The SGMX framework offers potential benefits for facilitating cross-disciplinary collaboration in SG design and improved accommodation of learner diversity and accessibility needs. It holds promise for maximising return-on-investment (ROI) in educational game development by allowing for individualised learning experiences. The study anticipates that these advancements will lead to better-informed learning design practices, more effective Human Resource Development (HRD) and Vocational Education and Training (VET) programs, and may open the door for future ML and AI-powered, personalised learning environments.