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1. Open Game Data: A Technical Infrastructure for Open Science with Educational Games

   https://doi.org/10.1007/978-3-031-44751-8_1  

   Gagnon, David J; Swanson, Luke (October 2023, Serious Games. JCSG 2023. Lecture Notes in Computer Science)

   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. 

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2. Open Game Data: Defining a Pipeline and Standards for Educational Data Mining and Learning Analytics with Video Game Data

   https://doi.org/10.1109/CoG60054.2024.10645573  

   Gagnon, David J; Swanson, Luke; Harpstead, Erik (August 2024, IEEE)

   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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3. An Architecture for Repeatable, Large-Scale Educational Game Data Analysis: Building on Open Game Data

   https://doi.org/10.1007/978-3-031-74138-8_4  

   Swanson, Luke; Gagnon, David J (October 2024, Springer Nature Switzerland)

   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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4. Language Model-Based Player Goal Recognition in Open World Digital Games

   https://doi.org/10.1609/aiide.v19i1.27503  

   Kim, Yeo Jin; Goslen, Alex; Rowe, Jonathan; Mott, Bradford; Lester, James (October 2023, Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment)

   Devising models that reliably recognize player goals is a key challenge in creating player-adaptive games. Player goal recognition is the task of automatically recognizing the intent of a player from a sequence of observed player actions in a game environment. In open-world digital games, players often undertake suboptimal and varied sequences of actions to achieve goals, and the high degree of freedom afforded to players makes it challenging to identify sequential patterns that lead toward specific goals. To address these issues, we present a player goal recognition framework that utilizes a fine-tuned T5 language model, which incorporates our novel attention mechanism called Temporal Contrary Attention (TCA). The T5 language model enables the framework to exploit correlations between observations through non-sequential self-attention within input sequences, while TCA enables the framework to learn to eliminate goal hypotheses by considering counterevidence within a temporal window. We evaluate our approach using game trace data collected from 144 players' interactions with an open-world educational game. Specifically, we investigate the predictive capacity of our approach to recognize player goals as well as player plans represented as abstract actions. Results show that our approach outperforms non-linguistic machine learning approaches as well as T5 without TCA. We discuss the implications of these findings for the design and development of player goal recognition models to create player-adaptive games. 

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5. Reflection in Game-Based Learning: A Survey of Programming Games

   https://doi.org/10.1145/3402942.3403011  

   Villareale, J.; Biemer, C.; Seif El-Nasr, M.; Zhu, J. (July 2020, Foundations of Digital Games)

   Reflection is a critical aspect of the learning process. However, educational games tend to focus on supporting learning concepts rather than supporting reflection. While reflection occurs in educational games, the educational game design and research community can benefit from more knowledge of how to facilitate player reflection through game design. In this paper, we examine educational programming games and analyze how reflection is currently supported. We find that current approaches prioritize accuracy over the individual learning process and often only support reflection post-gameplay. Our analysis identifies common reflective features, and we develop a set of open areas for future work. We discuss these promising directions towards engaging the community in developing more mechanics for reflection in educational games. 

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