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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.

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.

We report on design-based research to refine a professional development workshop that supports teachers to customize online curricula. We iteratively design representations to make the knowledge integration pedagogy of the curricula visible. We study ways to make the work of students using the curricula actionable for participating teachers. We analyze participants’ trajectories across the three iterations of the workshop. Initially, when participants realized they could customize the online curriculum, they developed feelings of ownership. Then, as participants deepened their understanding of the pedagogy, they began to use it to evaluate their own instruction. The trajectory culminated in participants connecting the pedagogy to student work from their own classroom. This led to a shift from focusing on remedies for misconceptions to seeking opportunities for building on students’ nascent ideas when customizing. The workshop refinements empowered teachers to mobilize the pedagogy to interpret their students' work to inform their customization decisions.

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.

Mindsets play an important role in persevering in computer science: while some learners perceive bugs as opportunities for learning, others become frustrated with failure and see it as a challenge to their abilities. Yet few studies and interventions take into account the motivational and emotional aspects of debugging and how learning environments can actively promote growth mindsets. In this paper, we discuss growth mindset practices that students exhibited in “Debugging by Design,” an intervention created to empower students in debugging—by designing e-textiles projects with bugs for their peers to solve. Drawing on observations of four student groups in a high school classroom over a period of eight hours, we examine the practices students exhibited that demonstrate the development of growth mindset, and the contexts where these practices emerged. We discuss how our design-focused, practice-first approach may be particularly well suited for promoting growth mindset in domains such as computer science.

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.