Author Information
PG Schrader, University of Nevada, Las Vegas, United StatesMark Carroll, University of Nevada, Las Vegas, United States
Abstract
This paper models a process for exploring questions situated in digital video games and broader educational contexts. Findings from an ongoing study are used to outline a process to extract data from the encapsulating social media ecosystem using applied data science, artificial intelligence (AI), and machine learning (ML) techniques. Previous efforts involving Delphi techniques to examine player behavior and social discourse across online game environments (Schrader et al., 2020) tend to be cumbersome and laborious. Alternatively, educational data science techniques, in conjunction with AI and ML, provide mechanisms to triangulate data from three distinct sources: player discussions about performance on social media, game telemetry data, and game outcomes. In this study, researchers applied supervised and unsupervised machine learning approaches alongside statistical and learning analytics methods to extract and analyze behavioral patterns to evaluate if players’ social media assertions about success aligned with the actions they take (Baker & Siemens, 2014; Siemens, 2013). Findings reveal that players’ claims often reflect personal anecdote rather than observable behaviors that are linked to success. This work carefully outlines the integrated process (e.g., ML with public APIs) as a model for educational researchers to engage in similar studies. We advocate for broader utilization of data science, AI, and machine learning in research contexts that represent large, complex, but publicly available datasets (e.g., telemetric user data). This session serves as an entry point for researchers who are new to AL and ML, especially those investigating learning in contexts with high-volume data streams.
Paper Information
Conference: ACE2025Stream: Design
This paper is part of the ACE2025 Conference Proceedings (View)
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To cite this article:
Schrader P., & Carroll M. (2026) A Video Game Telemetry Project: Modeling Data Science and Machine Learning in Educational Research ISSN: 2186-5892 – The Asian Conference on Education 2025: Official Conference Proceedings (pp. 1059-1073) https://doi.org/10.22492/issn.2186-5892.2026.83
To link to this article: https://doi.org/10.22492/issn.2186-5892.2026.83
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