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This paper introduces a novel approach for learning natural language descriptions of scenery in Minecraft. We apply techniques from Computer Vision and Natural Language Processing to create an AI framework called MineObserver for assessing the accuracy of learner-generated descriptions of science-related images. The ultimate purpose of the system is to automatically assess the accuracy of learner observations, written in natural language, made during science learning activities that take place in Minecraft. Eventually, MineObserver will be used as part of a pedagogical agent framework for providing in-game support for learning. Preliminary results are mixed, but promising with approximately 62% of images in our test set being properly classified by our image captioning approach. Broadly, our work suggests that computer vision techniques work as expected in Minecraft and can serve as a basis for assessing learner observations. 

Our work investigates interest triggering, a necessary component of sustaining and developing long-term interest in STEM. We gathered interview data from middle school aged learners (N = 7) at a science-focused Minecraft summer camp over a period of one week. We first identified STEM interest triggering episodes, then categorized each episode based on codes developed previously by Renninger and Bachrach (2016). Our initial findings show differences in the frequency of interest triggering episodes across individuals and suggest that personal relevance and the use of Minecraft played prominent roles. 

After a brief review of the science of interest and the game of Minecraft, we present a taxonomy of common Minecraft actions and activities and propose that they represent links to specific STEM disciplines. We then discuss the development of a Minecraft survey intended to identify STEM-related interests, and present the results of a pilot study using the survey in three Minecraft camps held in the summer of 2017. We describe the most and least popular Minecraft activities, and report initial analyses of the surveys, revealing potential connections in the earth, biological, and environmental areas of STEM. 

This paper addresses the relationship between one of the most popular video games in history (Minecraft) and STEM education. It describes a taxonomy of STEM-relevant Minecraft activities (e.g., designing and building automated farms) and their relationship to a wide range of STEM disciplines as defined by Classification of Instructional Programs (CIP; a product of the US Department of Education). Based on interviews with expert Minecraft players, academic research that analyzes Minecraft's educational uses, existing game documentation, and feedback from STEM experts, the taxonomy provides the foundation for better understanding how playing the game may inform the development of STEM interest, and how educators may best leverage those connections.