Journal of Research in Science Teaching最新文献

Meaningful participation in science and engineering practices requires that students make their thinking visible to others and build on one another's ideas. But sharing ideas with others in small groups and classrooms carries social risk, particularly for students from nondominant groups and communities. In this paper, we explore how students' perceptions of classrooms shape their contributions to classroom knowledge building in science across a wide range of classrooms. We examine the claim that when students feel a sense of belonging in class, they contribute more and perceive their ideas to be more influential in knowledge building. Data comes from classroom exit tickets (n = 10,194) administered in 146 classrooms as part of a 10-state field test of a new middle-school science curriculum, OpenSciEd, which were analyzed using mixed effects models. We found that students' sense of belonging predicted the degree to which they contributed ideas out loud in class (Odds ratio = 1.57) as well as the degree to which they perceived their contributions as influencing others (Odds ratio = 1.53). These relationships were particularly strong for students who reported a lower a sense of belonging. We also found significant differences by both race and gender in whether students said they contributed and believed their ideas influenced those of others. These findings suggest that a learner's sense of belonging in class and willingness to contribute may be mutually reinforcing, highlighting the need to promote content-specific strategies to foster belonging in ways that support collaborative knowledge building.

In response to Li, Reigh, He, and Miller's commentary, Can we and should we use artificial intelligence for formative assessment in science, we argue that artificial intelligence (AI) is already being widely employed in formative assessment across various educational contexts. While agreeing with Li et al.'s call for further studies on equity issues related to AI, we emphasize the need for science educators to adapt to the AI revolution that has outpaced the research community. We challenge the somewhat restrictive view of formative assessment presented by Li et al., highlighting the significant contributions of AI in providing formative feedback to students, assisting teachers in assessment practices, and aiding in instructional decisions. We contend that AI-generated scores should not be equated with the entirety of formative assessment practice; no single assessment tool can capture all aspects of student thinking and backgrounds. We address concerns raised by Li et al. regarding AI bias and emphasize the importance of empirical testing and evidence-based arguments in referring to bias. We assert that AI-based formative assessment does not necessarily lead to inequity and can, in fact, contribute to more equitable educational experiences. Furthermore, we discuss how AI can facilitate the diversification of representational modalities in assessment practices and highlight the potential benefits of AI in saving teachers’ time and providing them with valuable assessment information. We call for a shift in perspective, from viewing AI as a problem to be solved to recognizing its potential as a collaborative tool in education. We emphasize the need for future research to focus on the effective integration of AI in classrooms, teacher education, and the development of AI systems that can adapt to diverse teaching and learning contexts. We conclude by underlining the importance of addressing AI bias, understanding its implications, and developing guidelines for best practices in AI-based formative assessment.

There is growing recognition in the education community that the problem-solving practices that comprise computational thinking (CT) are a fundamental component of both life and work in the twenty-first century. Historically, opportunities to learn CT have been confined to computer science (CS) and elective courses that lack racial, ethnic, and gender diversity. To combat this inequity, a number of scholars have proposed integrating CT practices into core curriculum——especially science, technology, engineering, and math curriculum. Successfully achieving the goal of integrated CT, however, depends on frameworks to guide integration, professional development for teachers, exemplars of successful integrations, and identifications of the barriers teachers encounter. Research pertaining to each of these areas is in its infancy. This study addresses these needs through a collective case study of 10 secondary science teachers' implementations of a novel, process-based, unplugged approach to CT/science integration and the factors that supported or hindered their CT/science integration efforts. The results of this work reveal that: (1) an unplugged and process-based approach to CT/science integration shows promise as a vehicle for infusing CT into diverse science classrooms; (2) educators' teaching context exerts a strong influence on their CT-integration efforts and persistence; and (3) special attention is needed to support teachers in their CT/science integrations including algorithm creation. This study also demonstrates the utility of the Fraillon et al.'s CT framework as a guide for CT/science integration efforts and sheds light on the unique affordances of unplugged strategies for implementing CT-integrated science curricula.

This article explores the meaning of community-driven and owned science in the context of an Inuit-led land-based program, the Young Hunters Program. It is the foundational program of the Arviat Aqqiumavvik Society, situated in Nunavut, Canada, a community-led group dedicated to researching challenges to community wellness and designing and delivering programs to help address those challenges. We show how the program emerged locally and blends Indigenous knowledge systems (IKS) with tools of western science in respectful ways given its core sits within and emerges from what Inuit have always known to be true. We offer a description of six dimensions inherent in Inuit cultural practices and beliefs and foundational to the program activities and show how they open up various learning trajectories and possibilities for the involved young people to engage in community science. We then discuss in what ways the revitalization of IKS and practices led to community science projects that were locally meaningful and empowering with important implications for scientific work that mattered in light of locally experienced and devastating climate change threats. The study speaks to the importance of rebuilding relations and decolonizing knowledge systems and science practices, two key tools to Inuit self-determination and social transformations, and essential to achieving more social justice and equity in and beyond community science.

This article reports on analyses of the instructional practices of six middle- and high-school science teachers in the United States who participated in a research-practice partnership that aims to support reform science education goals at scale. All six teachers were well qualified, experienced, and locally successful—respected by students, parents, colleagues, and administrators—but they differed in their success in supporting students' three-dimensional learning. Our goal is to understand how the teachers' instructional practices contributed to their similarities in achieving local success and to differences in enabling students' learning, and to consider the implications of these findings for research-practice partnerships. Data sources included classroom videos supplemented by interviews with teachers and focus students and examples of student work. We also compared students' learning gains by teacher using pre–post assessments that elicited three-dimensional performances. Analyses of classroom videos showed how all six teachers achieved local success—they led effectively managed classrooms, covered the curriculum by teaching almost all unit activities, and assessed students' work in fair and efficient ways. There were important differences, however, in how teachers engaged students in science practices. Teachers in classrooms where students achieved lower learning gains followed a pattern of practice we describe as activity-based teaching, in which students completed investigations and hands-on activities with few opportunities for sensemaking discussions or three-dimensional science performances. Teachers whose students achieved higher learning gains combined the social stability characteristic of local classroom success with more demanding instructional practices associated with scientific sensemaking and cognitive apprenticeship. We conclude with a discussion of implications for research-practice partnerships, highlighting how partnerships need to support all teachers in achieving both local and standards-based success.

There is a significant amount of research literature on the importance of identifying and building on students' experiences and ideas for making sense of the natural world, especially when engaging in science practices. Simultaneously, approaches to creating justice-oriented science education promote the need to focus on the diverse sense-making repertoires that students, especially those from historically marginalized communities, bring to science classrooms. However, when it comes to emergent bi/multilingual students, science education has favored narrow definitions of what ways of communicating are seen as productive for figuring out natural phenomena, privileging English-based academic vocabulary. In this article, we investigate the myriad conceptual and semiotic resources that third-grade emergent bilingual students developed and used when explaining sound production. Additionally, we explore how students investigated the sounds produced by a string instrument and unpacked the how and whys that give rise to the pitch of the sounds they heard. Our analyses indicate that: (1) students created mechanistic explanations that identified how changes to the salient physical features of strings affected the pitch of the sounds; (2) students created and laminated multiple semiotic resources when sharing their observations and explanations, particularly sound symbolisms; and (3) students navigated both semiotic convergence and divergence as they worked toward conceptual convergence. Based on our findings, we argue that justice-oriented science learning environments must become spaces where emergent bilingual students can build on all their conceptual, semiotic, and cultural resources, without being policed, as they engage science practices.

This study investigated the effects of presenting domain information (basic information about the domain) either together with or instead of offering exploratory practice (an exploratory opportunity in a simulation-based representation of the learning domain) prior to inquiry learning for facilitating students' hypothesis generation and subsequent inquiry processes and their knowledge acquisition. Secondary school students (n = 118) completed a simulation-based inquiry task on force and motion. They were randomly assigned to one of four conditions: the D + E condition (n = 29), in which domain information and exploratory practice were available; the D condition (n = 30), in which only domain information was available; the E condition (n = 32), in which only exploratory practice was available; or the C condition (n = 27), with no support at all. Students' knowledge was measured with a pre- and posttest and a test on knowledge of variables. Inquiry processes were inferred from information students entered in a Hypothesis Scratchpad and an Observation tool, and from a final summary that they had to write. Results indicated that providing students with domain information alone helps to foster their knowledge of variables before generating hypotheses and leads to knowledge acquisition. The results also showed that the opportunity to explore before experimenting does not affect students' inquiry behavior or learning performance, even when combined with providing students with domain information.

Students often perceive school science as purely theoretical, overloaded with facts, and mostly disconnected from their school, home, and community life. One way to bridge the disconnection between school science and lived experiences, and support students in realizing the relevance of science to their everyday life, is by enabling them to integrate their funds of knowledge (FoK), the knowledge and expertise people have because of their roles in their families, communities, and culture, within science. To support students in this task, we developed Together with Science, a series of Vlogs (video blogs) designed to guide families and students through home-based experiments and support the emergence of a third space, where science and everyday life intersect. The Vlogs provided the necessary scientific background for the experiments and encouraged participants to share aspects of their lived experiences and reflect upon the realization of scientific phenomena in their lives. Using videotaped discussions of students and family members, we examined the interactions between them, as well as the FoK addressed in the conversation. The results indicate that multiple types of interactions emerged between students and family members, as they jointly conducted the science experiments and discussed their findings. Shifts in these interactions were associated with the development of a shared third space, in which both students and family members were able to concurrently relate everyday life to the scientific phenomenon. Most FoK were associated with participants' homes and culture. Our results suggest that family members can serve as brokers to support their children in realizing the relationship between science and their everyday life, by bringing in their unique FoK into the scientific discussion, and highlighting the importance of thoughtfully designed learning environments to support them in the process.

Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning—a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the measurement of knowledge and understanding to the measurement of performance and knowledge in use. Performance tasks requiring argumentation must capture the many ways students can construct and evaluate arguments in science, yet such tasks are both expensive and resource-intensive to score. In this study we explore how machine learning text classification techniques can be applied to develop efficient, valid, and accurate constructed-response measures of students' competency with written scientific argumentation that are aligned with a validated argumentation learning progression. Data come from 933 middle school students in the San Francisco Bay Area and are based on three sets of argumentation items in three different science contexts. The findings demonstrate that we have been able to develop computer scoring models that can achieve substantial to almost perfect agreement between human-assigned and computer-predicted scores. Model performance was slightly weaker for harder items targeting higher levels of the learning progression, largely due to the linguistic complexity of these responses and the sparsity of higher-level responses in the training data set. Comparing the efficacy of different scoring approaches revealed that breaking down students' arguments into multiple components (e.g., the presence of an accurate claim or providing sufficient evidence), developing computer models for each component, and combining scores from these analytic components into a holistic score produced better results than holistic scoring approaches. However, this analytical approach was found to be differentially biased when scoring responses from English learners (EL) students as compared to responses from non-EL students on some items. Differences in the severity between human and computer scores for EL between these approaches are explored, and potential sources of bias in automated scoring are discussed.