Research
This paper describes the HeapCraft tool suite in detail.
In this paper, we explore a large HeapCraft dataset to formulate and verify a general measure of collaboration based directly on players' in game actions.
We demonstrate the data analysis and visualization tools provided by our framework and point the way to using data from Minecraft servers to answer social science questions. We show how player behaviors such as exploration, mining, building and fighting can be recognised from fundamental game actions such as a lifting and hitting bricks, moving and jumping. The Slides contain excerpts from our subsequent work on player collaboration.
A poster presentation summarizing some of the HeapCraft tools.
Based on our existing work, we expand our data collection effort by providing new plugins to improve gameplay and real-time player analytics to Minecraft server administrators. The acquired data is then used to study player collaboration. Actionable ways to improve player collaboration are proposed based on insights from our data analysis.
This first manuscript builds the foundation of the HeapCraft project. It details the development of the Epilog data collection plugin for collecting data on servers and player in-game actions. Multiple applications are demonstrated for statistical data analysis in Minecraft, including classifying player behavior.