Games+Learning+Society 11 Conference

Using Spatial Game Analytics to Analyze Player Paths Through Games
Dennis Ramirez, Matthew Berland

By combining pre-test and post-test measures with spatial gaming analytics, we will investigate how different player types move through a level, and how those differences can inform the design of educational games like Fair Play. Of particular interest were the differences between participants who had a high final bias compared to a low final bias, and participants who play games more than an hour a week, and those that do not. Initial analysis revealed differences in the way these groups played Fair Play.

Data Collection in The Radix Endeavor: A Working Example
Louisa Rosenheck, Susannah Gordon-Messer, Jody Clarke-Midura

The Radix Endeavor (radixendeavor.org) is an online multiplayer game designed to improve knowledge and engagement in high school math and science students. In a robust virtual world, players explain and solve problems by completing narrative driven quests to progress through the world. As they take on the roles of scientists and mathematicians in authentic situations, they develop inquiry, problem solving and critical thinking skills. Through a dashboard, the game allows teachers to monitor student progress as well as access resources that connect the gameplay to classroom instruction.


ADAGE (Assessment Data Aggregator for Game Environments)
Mike Tissenbaum, Dennis Ramirez, Mark Stenerson, Isaac Goodin, Matthew Berland

There is a growing interest in investigating how games help learners learn; however, many researchers are unable to decipher the myriad of co-occurring events happening when learners engage in digital games, or how they might provide insight into the learning taking place. In response, we introduce ADAGE (Assessment Data Aggregator for Game Environments), a set of data collection and analysis tools on a shared, open source platform that use big data techniques to transform clickstream data from game and design systems into formative feedback for usability testing and evidence of learning. ADAGE aims to formalize the data formats captured and output when learners play with games. Creating a standard will a) make it easier for researchers to collect and analyze the data; and b) allow designers and developers to create a common API for working with ADAGE data, which can allow for a wider generalizability of tools and methodologies.