Visualizing log-file data from a game using timed word trees
Paula Ceccon Ribeiro, Melissa L Biles, Charles Lang, Claudio Silva, Jan L Plass
In this article, we present the application of a method for visualizing gameplay patterns observed in log-file data from a geometry game. Using VisCareTrails, a data visualization software system based on the principle of timed word trees, we were able to identify five novel behaviors that informed our understanding of how players were approaching the game. We further utilized these newly identified player behaviors by triangulating them with geometry test scores collected from players outside the game setting. We compared the predictive capacity of these behaviors against five demographic characteristics commonly observed to be associated with educational outcomes: age, gender, ethnicity, mother’s education, and attitude toward video games. Two of the novel behaviors we identified, both reflecting inflexible problem-solving strategies, outperformed all demographic variables except age in terms of predicting change in geometry test scores post-gameplay. We believe that this is sound evidence for the utility of VisCareTrails and the timed-word-tree method for identifying pedagogically relevant player behaviors from semi-structured data associated with educational games.
Visualizing the strategic landscape of arbitrary games
Joseph Carter Osborn, Benjamin Samuel, Michael Mateas
We present Gamalyzer, a game-independent and efficient visualization of sets of play traces. Unlike previous work on game-independent visualization, we focus on sequences of game actions as opposed to sequences of game states. Action sequences directly represent players’ strategic decisions. Moreover, since game actions may already be recorded as part of games’ telemetry and metrics, Gamalyzer is easier to integrate into existing analysis toolchains than state-sequence-based visualizations. Gamalyzer displays each play trace as a vertical line, with symbols along the line indicating game events. Similar play traces (according to the Gamalyzer metric, a specialization of edit distance) are arranged together along the horizontal axis, and as traces become more and less similar to each other over time, they bend towards and away from each other. The Gamalyzer metric is also used to present only the most interestingly different traces in the visualization, with the rest grouped together under their most similar cousins. We position Gamalyzer as an ideal trace filtering and selection tool to be used in concert with a state-centric (and possibly game-specific) visualization for context. This article also provides a detailed account of the Gamalyzer metric and new advice for defining game action schema to maximize the benefits obtained from the tool, along with two detailed case studies of the Gamalyzer visualization in practice.
Automatic generation of battle maps from replay data
With the advent of online gaming, access to in-game data has become increasingly important for players as it provides great opportunities for them to reflect and improve upon their gameplay or to compare their performance with others. Some of the currently most popular games focus on strategy and tactics, requiring players to skillfully position and maneuver units in order to achieve victory in battle. However, current visualizations for retrospective analysis of battles and that are targeted toward players are mainly limited to heatmaps and hence are not well suited for conveying the flow of battle. By contrast, military planners and historians alike have long used maps to provide a concise visual overview of troop movements. In this article, we are proposing an algorithm for automatically creating such battle maps from tracked in-game data. Several parameters allow to adjust the level-of-detail in the resulting maps. To demonstrate the practicality of our approach for post hoc analysis, we apply it to actual gameplay data obtained from a massively multiplayer online game and collected preliminary feedback among players of the game through an online survey.
Enjoying death among gamers, viewers, and users: A network visualization of Dark Souls 3’s trends on Twitch.tv and Steam platforms
In current Game Research, gaming service platforms such as PlayStation Network, Steam, and Twitch.tv represent a still poorly investigated topic. Despite the millions of monthly viewers and members, little efforts have been done to shed light on their dynamics and trends. This article aims to address such a lack by presenting the findings of an empirical inquiry guided by the key concepts of “platform” and “actor–network theory” with the support of a novel network visualization technique. Specifically, the role-playing game Dark Souls 3–related activity on Steam and Twitch.tv was collected for the first 20 days from the release (12 April–1 May 2016). Targeted data concerned several variables among which: most viewed streamers, streaming types, debating topics and reviews’ highlights on Steam (etc.) through screenshots, user-generated content, and text gathering. Data were processed and then visualized with the network-oriented software Gephi for uncovering associations and patterns in the targeted online environments. The action game The Division worked as an exploratory case study and counter-example for stressing the proposal. Although with some limitations, the visualization strategy adopted (four networks for each platform) proved to be effective in framing and communicating the results in a straightforward way. Finally, findings enlightened a phenomenon (i.e. gaming service platforms), that is, getting increasingly central in digital entertainment, and might inform further investigations with alternative designs and focuses.