Developing the Integrated Analysis Matrix (I-AM): A Data-Minding Approach for Better Ludonarrative Design-Based Research in Education

This paper introduces the Integrated Analysis Matrix (I-AM), a novel hybrid AI-assisted and human-led methodological approach towards the mindful integration, analysis, and interpretation of complex multimodal, mixed methods datasets – applied here to educational research, particularly where Game-based Learning (GBL) environments generate large volumes of entangled qualitative and quantitative data. Drawing on ludonarrative Design-based Research rooted in Sociocultural, Ecological Systems, and Complexity theories, the iterative development of the I-AM is illustrated through an example case within a case study extracted from the Design-based Research (DBR) study in which it was developed. The data work presented here highlights one Tabletop Role-Playing Game (TRPG) participant (codenamed GAG05B), whose datasets were extracted from a larger dataset and then retained – with informed consent – to illustrate the complexity of the data covered, even for a sample of one. The I-AM aligns diachronic changes in learner expression with shifts in metacognitive awareness and strategy use, as well as narrative complexity, supporting high-resolution insight into developmental trajectories that are typically obscured in traditional analytic approaches. By combining numeric (e.g., readability indices, metacognitive awareness inventories) and narrative data (e.g., interview and gameplay transcripts, player character journal entries), the I-AM is shown to address core challenges of scale and integrative coherence in mixed methods research. Findings demonstrate how this considered approach to human-AI collaboration can streamline analysis while preserving interpretive depth, allowing the I-AM to offer a replicable and scalable framework for researchers navigating the analytical complexity of design-based, multimodal, and mixed-methods inquiry.