Developing students' STEM identities and career awareness within a science discipline can be challenging for teachers as STEM identity extends beyond specific content expertise and traditional pedagogical skills. This study investigated how teachers' STEM identities along with their shared understanding of the goals for including STEM identity activities in a geoscience curriculum unit contributed to the design of classroom activities intended to develop their students' STEM identities. Data for this study includes video recordings from a teacher professional development workshop in which six middle school teachers from a large district in central California explored their own STEM identities, survey data on students' STEM identities, and artifacts from co-designed activities tailored to teachers' classes. Results reveal that teachers' STEM identities significantly influence how they support the development of students' STEM identities. In addition, providing teachers with the opportunity to describe their own STEM identity led to meaningful focus for the co-design process. Finally, while the focus of this workshop was a geoscience unit, this study argues that the co-design process as applied to STEM identity activities can apply to other STEM topics.
Educators are being encouraged to teach with "big" datasets that have more cases and attributes than are typically used in the classroom. When introduced carefully, these types of datasets can allow students to engage in complex and self-directed reasoning, develop data management and inquiry skills, and experience data analysis in a way that is more authentic to professional practice. However, "big data" is also often unwieldy. It can overwhelm students, overload their software tools, and interfere with planful analysis. How can we make such datasets manageable? This paper presents pedagogical strategies and technical methods for educators, educational designers, and young investigators to reduce the number of cases or attributes in a large dataset in ways that do not unduly compromise their analyses. We illustrate these strategies with interactive demonstrations in two freely-available open source tools: the Choosy plugin in CODAP, and a Python Jupyter notebook.
ABSTRACT Designing controlled experiments is central to scientific investigation, yet little is known about how students change their experimentation strategies as they work through a simulation‐based task and how different patterns of strategy use relate to investigation‐relevant reasoning. To address these gaps, we examined the strategies used by N = 681 middle and high school students in the United States as they used the Wildfire Explorer simulation to investigate how vegetation type affects wildfire spread. Students' time‐stamped interaction logs captured how they configured and conducted simulation trials. We used k‐means clustering to identify strategy profiles, full‐path sequence mining and heuristic process mining to characterize how strategies were organized over time, and multilevel models to examine associations with observation accuracy, evidence quality, and explanation level. We identified four strategy profiles characterized by low‐frequency use of coded strategies, one‐spark experimentation, mixed use dominated by three‐spark noncontrastive configurations, and near‐exclusive use of standard vary‐one‐thing‐at‐a‐time configurations. Adding profile membership improved model fit for all three reasoning outcomes after accounting for students' background characteristics and teacher‐level clustering. Relative to the low‐frequency profile, the standard vary‐one‐thing‐at‐a‐time profile was positively associated with all three outcomes; the two intermediate profiles showed more selective associations. Together, these findings show that students' experimentation differed in both variable control and the generation of observable comparative evidence, variation that would be obscured by a simple optimal‐versus‐suboptimal classification. These profiles provide a differentiated account of students' investigation practices and identify patterns that could be examined in future studies of instructional support.
In this work, a team of eight researchers, educators, and designers sought to collaboratively generate a set of design requirements for an AI agent system that could support students to learn about front-end engineering design skills. While there is a wealth of past research on AI in education, there are important gaps in these areas. For instance, work on AI in education has tended to focus on supporting learning in the context of well-defined problems. Our team aimed to explore something more challenging: how an AI agent could support students working with open-ended problems. The larger project involves developing a collaborative web-based platform, Mobile Design Studio (MODS), to support middle school and high school students to engage with front-end design. Results of implementation with students will be presented in future papers. Here, we describe how we generated a consensus-based set of design requirements for the technology developers. Developing consensus was particularly important, given that our team had a wide range of experiences and epistemologies, and the need for a shared vision of what the AI agent would do. We used collaborative autoethnography, where each author is a researcher-participant, both data generator and data analyst. Our design process used an iterative series of reflections and recorded discussions in which we explored how this AI agent could operate to support student learning. Additionally, these reflections considered our social positions and values as educators and researchers, and how these align with the project. In the results, we present (1) themes that emerged from our reflections on how our values aligned with the project, and (2) the results of the consensus design process. The six resulting design requirements address students, teachers, and the AI agent itself, and are linked to relevant learning and design concepts. The discussion highlights common themes and connections among the requirements, including an emphasis on user agency and human-AI collaboration. Finally, for those interested in applying these requirements to other projects, we discuss ethical guidelines to ensure that new systems do not reinforce inequalities among students.
Responding to the need for interdisciplinary, real-world STEM-integrated learning, we examine the relationship between teachers’ adaptive expertise in bioinformatics (a STEM field that integrates biology, data literacy, and computational approaches) and students’ classroom experiences. We draw on data from an earlier study, in which we conducted a summer professional development workshop and subsequent school-year implementation of a bioinformatics curriculum with five biology teachers. This study uses a mixed methods approach combining student pre/post suerveys, factor and regression analyses, and previously collected teacher adaptive expertise data to investigate how teachers’ abilities to adapt the STEM-integrated curriculum influenced their students’ classroom experiences. Using an adaptive expertise lens, we reveal challenges teachers faced in adapting STEM-integrated curricula in their practice due to a need for greater competence and confidence in content knowledge. Students’ classroom experiences showed significant growth in three of the five factors related to learning about bioinformatics with small to medium effect sizes. Notably, the two factors related to interests in working with real-world data and opportunities to develop literacy skills did not show significant growth. A regression analysis demonstrated statistically significant effects of teachers’ adaptive expertise on the student experience outcomes measured. We suggest that high-quality professional development should support teachers to become adaptive experts in STEM-integrated curricula, with a particular focus on improving teachers’ content knowledge. Furthermore, we contend that more research that can highlight specific knowledge needs of teachers (e.g., more adaptive expertise) is necessary if we are to fully realize the enormous potential for STEM-integrated learning in K–12 classrooms.
