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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). 

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). 

This paper describes exploratory research that contributes to a more holistic model of professional development (PD) for middle school STEM teachers to support inclusive makerspace classrooms. Despite an increased focus on maker education in K-12 settings, teachers have reported limited support to deliver such instruction, especially with academically diverse learners. This case study examined instructional supports for teachers, including professional development and coaching focused on makerspace classroom activities, as well as structural conditions, the integration of metacognitive learning strategies, positive behavior supports, and Universal Design for Learning (UDL). Analysis of teacher interviews, surveys, and classroom observations revealed that teachers acknowledged the need for ongoing PD and the inclusion of UDL components into their lesson planning. 

Given the proliferation of makerspace experiences in K-12 education, there is a growing need to ensure accessibility for all learners, including those with disabilities and those at risk of academic failure. The limited research on these populations suggests that it is essential to examine how a broader range of learners participate in K-12 maker activities and any barriers that they face. We employed a cross-case qualitative methodology to investigate issues of participation and engagement by collaborating with four teachers who incorporated maker activities into STEM or science classes in four different middle schools. Across the four schools, teachers reported multiple challenges faced by learners including student-specific, instructional, and systemic barriers. Despites these challenges, however, we found evidence of students with disabilities meaningfully participating in maker activities. Implications for future research and practices are discussed from an ecological model perspective.