No abstract

Researchers have called for additional empirical studies associated with video games. However, every game is novel in terms of mechanics, content, context, and agency; known variables may be operationalized differently based on the game involved. It is incumbent upon researchers to leverage or create the best tools when extracting data from games. This article models instrument development of a scale intended to catalog users’ actions in a novel context (i.e., the game League of Legends) using a mixed methods approach. Specifically, this work outlines data collection and validation strategies using an online social news aggregation, rating, and discussion resource (i.e., Reddit), involving multiple cycles to elicit expert input and queries to generate consensus from expert gamers, followed by analysis of responses and an exploratory factor analysis for scale construction. Reddit provided three unique functions relative to the process: (a) access to experts who informed the development of scale items, (b) a social space and mechanism to validate scale items, and (c) the opportunity to capture data necessary to establish the psychometric properties of the instrument. Findings associated with scale development (i.e., item generation, theoretical and psychometric analyses) are presented. Overall, implications for instrument development in continually evolving contexts are discussed.

The current pilot study examined how well a reflective moral-choice video game predicted the rating scale scores of aggression types. To begin, the authors used a coding system to examine in-game proactive and reactive behaviors. This analysis resulted in a tallied score for each construct. These game-based scores were then included in regression models, examining how well within-game behaviors predict scores on a pre-existing rating scale of both proactive and reactive aggression. Findings indicated that game-based proactive scores were not predictive of proactive aggression ratings; however, reactive game-based scores were predictive of reactive aggression ratings. Implications for these findings are discussed.

COVID-19 required teachers and administrators to swiftly transition traditional education into learning management systems (LMS). However, LMSs are designed as delivery trucks, providing content to students and serving as digital filing cabinets that organize and deliver information in an appropriate manner. This inherent limitation allows for the disenfranchisement of underprivileged groups, which can be addressed via the transformative social and emotional learning framework (TSEL). TSEL is a method of alleviating inequitable learning experiences by accounting for racial oppressions that marginalized groups encounter within education. Here, the authors discuss how to leverage additional digital learning environments to enhance and support identity, belongingness, intersectionality, and agency during online learning. These digital learning environments are detailed from a research perspective as well as a practical one, allowing readers to immediately implement concurrent extensions to their LMS.

The current pilot study examined how well a reflective moral-choice video game predicted the rating scale scores of aggression types. To begin, the authors used a coding system to examine in-game proactive and reactive behaviors. This analysis resulted in a tallied score for each construct. These game-based scores were then included in regression models, examining how well within-game behaviors predict scores on a pre-existing rating scale of both proactive and reactive aggression. Findings indicated that game-based proactive scores were not predictive of proactive aggression ratings; however, reactive game-based scores were predictive of reactive aggression ratings. Implications for these findings are discussed.