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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. 

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. 

While making physical computational artifacts such as robots or electronic textiles is growing in popularity in CS education, little is known about student informal conceptions of these systems. To study this, we video-recorded think-aloud sessions (~10 minutes each) of 22 novice CS high school students explaining their understanding of everyday physical computing systems and qualitatively analyzed transcripts and student drawings for their structural, behavioral, and functional understanding of these systems. Most students identified the presence of programs in making these systems functional but struggled to account them structurally and behaviorally. A few students pointed out probable programming constructs in shaping underlying mechanisms, drawing from their prior programming experiences. To integrate these systems in computing education, we call for pedagogical designs to address the invisibility of computation—both of structural interconnections and of program execution. 

We explore how a Teacher Action Planner (TAP) that synthesizes student ideas impacts teacher noticing. The TAP uses Natural Language Processing (NLP) to detect student ideas in written explanations. We compared teacher noticing while using the TAP to noticing when reviewing student explanations. The TAP helped teachers deepen their analysis of student ideas. We did not see any impact on immediate instructional practice. We propose redesigns to the TAP to better connect noticing to instruction. 

Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design self-explanation support that simultaneously facilitates performance and learning outcomes. Towards this goal, we designed anticipatory diagrammatic self-explanation, a novel form of self-explanation embedded in an Intelligent Tutoring System (ITS). In our ITS, anticipatory diagrammatic self-explanation scaffolds learners by providing visual representations to help learners predict an upcoming strategic step in algebra problem solving. A classroom experiment with 108 middle-school students found that anticipatory diagrammatic self-explanation helped students learn formal algebraic strategies and significantly improve their problem-solving performance. This study contributes to understanding of how self-explanation can be scaffolded to support learning and performance. 

Overly simplistic school science laboratories constrain student agency. We share and discuss a case from 9th grade science classroom in which students all conducted highly varied independent investigations that were each highly coherent and scientifically well-motivated. We discuss the conditions that led to their experiments in terms of instability and uncertainty. Our findings suggest that it may be beneficial to support and recognize multiple forms of uncertainty simultaneously to encourage multiple forms of investigation to respond to those uncertainties. Finally, an “instability” caused by having multiple candidate models or explanations in play may be more generative than uncertainties based on gaps in knowledge. 

Despite the prevalence of conducting classroom studies using educational technology, it is underexplored what practical benefits classroom studies with educational technology offer to teachers and students, and what concerns or challenges they perceive. Our interviews found that teachers view study participation as a meaningful learning opportunity but also shared challenges and concerns, some of which are related to remote learning during the COVID-19 pandemic. We offer strategies that researchers can employ when conducting classroom studies.