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This paper explores an episode of epistemic injustice that develops between two students with help from two teachers. Our analysis seeks to demonstrate not only that epistemic injustice has occurred, but also, how, and why it matters. In particular, we explore the idea of credibility deficit as helping to account for how and why one student’s contributions were routinely sidelined or ignored, and how that repeated positioning led to the ultimate act of testimonial injustice and its outcome, a wrong in the form of a loss of opportunity to learn. 

We facilitated a remote educational summer camp for teenage youth, with participants “sheltering in place” at home due to the COVID-19 pandemic. The summer camp was part of an initiative aimed at promoting STEM education for youth through learning about their pets’ senses and engaging in a co-design project to enrich aspects of their pets’ lives. We describe how situating scientific and design activities within the home and with pets engages participants in ethnomethodological practices such as field work, naturalistic observation, and in situ design that build upon their funds of knowledge. We discuss implications for the designs of learning environments that leverage the benefits of at-home science and design with pets. 

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. 

In this paper, we present a co-design study with teachers to contribute towards development of a technology-enhanced Artificial Intelligence (AI) curriculum, focusing on modeling unstructured data. We created an initial design of a learning activity prototype and explored ways to incorporate the design into high school classes. Specifically, teachers explored text classification models with the prototype and reflected on the exploration as a user, learner, and teacher. They provided insights about learning opportunities in the activity and feedback for integrating it into their teaching. Findings from qualitative analysis demonstrate that exploring text classification models provided an accessible and comprehensive approach for integrated learning of mathematics, language arts, and computing with the potential of supporting the understanding of core AI concepts including identifying structure within unstructured data and reasoning about the roles of human insight in developing AI technologies. 

Materials play an important role in learning. Humans actors use materials in particular ways depending on the context and materials also can shape how human actors use materials. This study explores the dialogical relationship between the participants and materials in suminagashi, a Japanese paper marbling activity. We found that materials that are traditionally thought of as art materials, such as paintbrushes, are used to support practices often considered science practices, such as experimentation. 

This work is part of an ongoing partnership that seeks to create a sustainable infrastructure to support GIS-infused instruction in a large urban school district. In this paper, we report an illustrative cross-case comparison of two teachers’ approaches to infusing GIS in their courses. The goal of this analysis is to examine how GIS-infused instruction is adapted in different contexts and to consider the affordances of divergent approaches. Findings illustrate the relationships among organizational context, individual and collective context, particularly teacher identity, and instructional practice in the work of spreading GIS-infused instruction. We also discuss key lessons learned in our partnership thus far and implications for district-level partnerships focused on spread and scale. 

Researchers in the Learning Sciences take two prevalent stances: research as building theories or as developing designs. The connection between theories and designs is most often filled in by methods, but an alternative stance is possible: research as improving models. The modeling stance seeks parsimonious, useful, illuminating descriptions of learning activity systems. Models can help us understand and express how variability (in all its forms) plays into, is enacted during, and results from designed learning activities. Building models often requires employing multiple theories, methods, and design elements; a modeling stance recognizes that our research often elaborates a multi-level systems view. An explicit modeling stance may lead to developing descriptions of complex systems, inviting multi-stakeholder teamwork to improve these systems, integrating advances in learning analytics and educational data mining, and adding to ability of learning sciences research to tackle challenges at scale.