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Middle school students learned about astronomy and STEM concepts while exploring Minecraft simulations of hypothetical Earths and exoplanets. Small groups (n = 24) were tasked with building feasible habitats on Mars. In this paper, we present a scoring scheme for habitat assessment that was used to build novel multi/mixed-input AI models. Using Spearman’s rank correlations, we found that our scoring scheme was reliable with regards to team size and face-to-face instruction time and validated with self-explanation scores. We took an exploratory approach to analyzing image and block data to compare seven different input conditions. Using one-way ANOVAs, we found that the means of the conditions were not equal for accuracy, precision, recall, and F1 metrics. A post hoc Tukey HSD test found that models built using images only were statistically significantly worse than conditions that used block data on the metrics. We also report the results of optimized models using block only data on additional Mars bases (n = 57). 

MineObserver 2.0 is an AI framework that uses Computer Vision and Natural Language Processing for assessing the accuracy of learner-generated descriptions of Minecraft images that include some scientifically relevant content. The system automatically assesses the accuracy of participant observations, written in natural language, made during science learning activities that take place in Minecraft. We demonstrate our system working in real-time and describe a teacher dashboard to showcase observations, both of which advance our previous work. We present the results of a study showing that MineObserver 2.0 improves over its predecessor both in perceived accuracy of the system's generated descriptions as well as in usefulness of the system's feedback. In future work, we intend improve system generated descriptions to give more teacher control and shift the system to perform continuous learning to more rapidly respond to novel observations made by learners. 

Middle school students learned about astronomy and STEM concepts while exploring Minecraft simulations of hypothetical Earths and exoplanets. Small groups (n = 24) were tasked with building feasible habitats on Mars. In this paper, we present a scoring scheme for habitat assessment that was used to build novel multi/mixed-input AI models. Using Spearman’s rank correlations, we found that our scoring scheme was reliable with regards to team size and face-to-face instruction time and validated with self-explanation scores. We took an exploratory approach to analyzing image and block data to compare seven different input conditions. Using one-way ANOVAs, we found that the means of the conditions were not equal for accuracy, precision, recall, and F1 metrics. A post hoc Tukey HSD test found that models built using images only were statistically significantly worse than conditions that used block data on the metrics. We also report the results of optimized models using block only data on additional Mars bases (n = 57). 

Minecraft continues to be a popular digital game throughout the world, and the ways in which adolescents play can provide insight into their existing interests. Through informal summer camps using Minecraft to expose middle school students to concepts in astronomy and earth science, we collected self-reports of STEM and Minecraft interest, as well as behavioral log data through player in-game interactions. Finding relationships between in-game behaviors and individual interest can provide insight into how educational experiences in digital games might be designed to support learner interests and competencies in STEM. Bayesian model averaging of data across camps was implemented to address the relatively small sample size of the data. Results revealed the important role of existing interest and knowledge for developing and sustaining interest. 

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.