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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.
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Open Game Data: Defining a Pipeline and Standards for Educational Data Mining and Learning Analytics with Video Game Data
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.
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- PAR ID:
- 10593375
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-5067-8
- Page Range / eLocation ID:
- 1 to 8
- Subject(s) / Keyword(s):
- games educational data mining learning analytics
- Format(s):
- Medium: X
- Location:
- Milan, Italy
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CoG60054.2024.10645573
