Differentiating video game expertise using the Model of Domain Learning

Introduction Expertise in video games is commonly stratified using self-reported experience, which is prone to bias and measurement error. This study introduces a behavioral observation approach to provide a more objective and psychometrically sound method for differentiating expertise. Specifically, the Behavioral Observation Matrix-Proxemics (BOM-Proxemics) was developed to assess expertise through observable in-game behaviors grounded in digital proxemics theory. Methods Using recorded gameplay from Apex Legends , 102 players were evaluated as novice, competent, or expert based on in-game observed behaviors. The BOM-Proxemics measured behaviors across four domains: Spatial Positioning, Spatial Realization, Spatial Appropriation, and Spatial Interactivity. Reliability and validity were evaluated through inter-observer agreement, internal consistency, and criterion-related analyses. Predictive relationships were examined using ordinal logistic regression, and group differences were analyzed using ANOVA. Results The BOM-Proxemics demonstrated strong reliability and validity, including excellent inter-observer agreement and high internal consistency. Total scores were a significant positive predictor of in-game rank. Among subscales, Spatial Positioning, Spatial Appropriation, and Spatial Interactivity significantly predicted rank, while Spatial Realization did not. Significant differences in scores were observed across all expertise groups, with pairwise comparisons indicating clear separation between novice, competent, and expert players. Discussion/Conclusion Findings support the BOM-Proxemics as a psychometrically sound, behavior-based measure of video game expertise. The results demonstrate the instrument's ability to differentiate expertise levels and predict performance outcomes, offering a viable alternative to self-report measures. Implications include advancing expertise research through direct observation and extending behavior-based assessment approaches to other complex digital environments.