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Given the incredible popularity of video games in contexts from entertainment to education, and the capacity of internet-connected games to record fine-grained telemetry data, there exists an unprecedented opportunity to investigate gameplay behaviors, outcomes, and their relationships to learning processes. However, with these opportunities come the need for technical infrastructures to manage the collection and analysis of massive amounts of game event data. In this work, we build upon existing literature to develop an architectural design for such infrastructure. We address issues of play data collection across many games; regular, repeatable extraction of gameplay features from raw data; and access to data for secondary analyses. In addition, we describe an implementation of this infrastructure and provide real-world examples of the implementation’s usage in prior large-scale analysis work. 

Students in open-ended educational games have a number of different pathways that they can select to work productively through a learning activity. Educators and system designers may want to know which of these pathways are most effective for engagement, learning, or other desirable outcomes. In this paper, we investigate which prior jobs and factors are associated with higher rates of student quitting behavior in an educational science exploration game. We use a series of Chi squared analyses to identify the jobs with the highest rates of quitting overall, and we calculate logistic regressions within specific jobs to determine the potential factors that lead to students quitting those jobs. Our analysis revealed that for 23 of the 40 jobs examined, having experience in at least one previous job significantly decreased the chances of students quitting the subsequent job, and that completing specific prior jobs reduces quit rates on specific later jobs. In our discussion, we describe the challenges associated with modeling quitting behavior, and how these analyses could be used to better optimize students’ pathways through the game environment. Specially, guiding students through specific sequences of preliminary jobs before tackling more challenging jobs can improve their engagement and reduce dropout rates, thus optimizing their learning pathways. 

In this paper we describe the need for a framework to support collaborative educational research with game data, then demonstrate a promising solution. We review existing efforts, explore a collection of use cases and requirements, then propose a new data architecture with related data standards. The approach provides modularity to the various stages of game data generation and analysis, exposing intermediate transformations and work products. Foregrounding flexibility, each stage of the pipeline generates datasets for use in other tools and workflows. A series of interconnected standards allow for the development of reusable analysis and visualization tools across games, while remaining responsive to the diversity of potential game designs. Finally, we demonstrate the feasibility of the approach through an existing implementation that uses this architecture to process and analyze data from a wide range of games developed by multiple institutions, at scale, supporting a variety of research projects. 

In this paper we describe a technical infrastructure, entitled Open Game Data, for conducting educational game research using open science, educational data mining and learning engineering approaches. We describe a modular data pipeline which begins with telemetry events from gameplay and ends with real time APIs and automated archival exports that support research. We demonstrate the usefulness of this infrastructure by summarizing several game research projects that have utilized and contributed back to Open Game Data. We then conclude with current efforts to expand the infrastructure. 

In this paper we describe a technical infrastructure, entitled Open Game Data, for conducting educational game research using open science, educational data mining and learning engineering approaches. We describe a modular data pipeline which begins with telemetry events from gameplay and ends with real time APIs and automated archival exports that support research. We demonstrate the usefulness of this infrastructure by summarizing several game research projects that have utilized and contributed back to Open Game Data. We then conclude with current efforts to expand the infrastructure.