Journal of Research in Science Teaching最新文献

The purpose of this study was to test predictability of environmental moral reasoning patterns of preservice science teachers (PSTs) by their epistemological beliefs and values. Four environmental moral dilemma scenarios that reflect different environmental moral dilemma situations taking place in four outdoor recreation contexts (i.e., hiking, picnicking, fishing, camping) were used to trigger and examine environmental moral reasoning of PSTs. Centers of moral concerns (i.e., ecocentric, anthropocentric, egocentric) and underlying reasons of environmental moral considerations (e.g., aesthetical concerns, justice issues) were used to investigate PSTs' environmental moral reasoning patterns. Data were collected from 1524 PSTs enrolled in six universities located in Central Anatolia region of Türkiye. A path model was proposed to test relationships of PSTs' epistemological beliefs and values to their environmental moral reasoning for each environmental moral dilemma scenario. Results indicated good-fit between study data and the path model tested for each environmental moral reasoning scenario. Variances in environmental moral reasoning scores that were explained by the path models had small to medium effect size values of 0.06 to 0.26. Statistical significance and direction of the tested relationships showed changes depending on the moral dilemma scenario context and focus of environmental moral reasoning. Nevertheless, path analyses consistently revealed positively significant relationships between environmental moral reasoning categories and epistemological beliefs in omniscient authority and self-transcendence and tradition values. Implications for science education policy and practice are discussed.

As professional science becomes increasingly computational, researchers and educators are advocating for the integration of computational thinking (CT) into science education. Researchers and policymakers have argued that CT learning opportunities should begin in elementary school and span across the K-12 grades. While researchers and policymakers have specified how students should engage in CT for science learning, the success of CT integration ultimately depends on how elementary teachers implement CT in their science lessons. This new demand for teachers who can integrate CT has created a need for effective conceptual tools that teacher educators and professional development designers can use to develop elementary teachers' understanding and operationalization of CT for their classrooms. However, existing frameworks for CT integration have limitations. Existing frameworks either overlook the elementary grades, conceptualize CT in isolation and not integrated into science, and/or have not been tested in teacher education contexts. After reviewing existing CT integration frameworks and detailing an important gap in the science teacher education literature, we present our framework for the integration of CT into elementary science education, with a special focus on how to use this framework with teachers. Situated within our design-based research study, we (a) explain the decision-making process of designing the framework; (b) describe the pedagogical affordances and challenges it provided as we implemented it with a cohort of pre- and in-service teachers; (c) provide suggestions for its use in teacher education contexts; and (d) theorize possible pathways to continue its refinement.

Previous studies have documented the promising results from student-constructed representations, including stop-motion animation (SMA), in supporting mechanistic reasoning (MR), which is considered an essential thinking skill in science education. Our current study presents theoretically and empirically how student-constructed SMA contributes to promoting MR. As a theoretical perspective, we propose a framework hypothesizing the link between elements of MR and the construction nature of SMA, that is, chunking and sequencing. We then examined the extent to which this framework was consistent with a multiple-case study in the domain of static electricity involving five secondary school students constructing and using their own SMA creation for reasoning. In addition, students' reasoning in pre- and postconstruction of an SMA was examined. Our empirical findings confirmed our framework by showing that all students identified the basic elements of MR, that is, entities and activities of entities, when engaging in chunking and sequencing. Chunking played a role in facilitating students to identify entities responsible for electrostatic phenomena, and sequencing seemed to elicit students to specify activities of these entities. The analysis of students' reasoning in pre- and postconstruction of SMA found that student-generated SMA has a potential effect on students' retention of the use of MR. Implications for instruction with SMA construction to support MR are discussed.

Taking on an agentic perspective, this study employed a digital ethnographic approach to examine a science teacher's emotional experiences in an online graduate science education course during the COVID-19 pandemic. Veronika, the teacher, revealed her feelings of grievance and loss to the graduate course cohort at the advent of large-scale school closures. Her emotions, shared through the online course, connected the members of the cohort to overcome emotional and pedagogical difficulties caused by the pandemic. She received both emotional and professional support from the cohort and designed an environmental related learning activity that centered on fun and connection in science learning. The activity stimulated students’ positive emotions and simultaneously served to reset Veronika's emotions. This study underlined that emotions connect teachers during a social crisis in ways that address obstacles encountered in teaching and learning. Lessons for teacher education include providing space for and acknowledging emotions in teaching, especially in times of stress and the importance of fostering agentic actions, collegiality, and collaboration by explicitly connecting an individual's emotions and beliefs to their professional practice.

It has been widely accepted in the science education research community that scientific literacy as a concept and phrase was introduced by Paul deHart Hurd in 1958. Recent research into the origins of the phrase, however, has shown this to be incorrect. Its first published use can be traced back, in fact, to 1945, and the phrase was frequently invoked in popular and research publications throughout the 1940s and 1950s. Exploring the historical circumstances of the phrase's introduction into popular discourse, it is argued, reveals that despite the rhetorical power and widespread adoption of the idea, scientific literacy (as others have pointed out) has proven to be little more than an empty slogan that offers no substantive guidance for thinking about the goals of science education. This essay argues that rather than continue to cling to the idea, the field of science education can more productively consider the most relevant and appropriate goals of science teaching by dispensing with the concept altogether.

There is extensive literature focusing on students' misconceptions in various subject domains. Several conceptual change approaches have been trying to understand how conceptual change occurs to help learners handle these misconceptions. This meta-analysis aims to integrate studies investigating the effectiveness of three types of conceptual change strategy: cognitive conflict, cognitive bridging, and ontological category shift in science learning. We conducted a random-effects meta-analysis to calculate an overall effect size in Hedges' g with a sample of 218 primary studies, including 18,051 students. Our analyses resulted in a large overall effect size (g = 1.10, 95% CI [1.01, 1.19], k = 218, p < 0.001). We also performed a robust Bayesian meta-analysis to calculate an adjusted effect size, which specified a large effect (adjusted g = 0.93, 95% CI [0.68, 1.07], k = 218). Results are also consistent across the conceptual change strategies of cognitive conflict (g = 1.10, 95% CI [0.99, 1.21], k = 150, p < 0.001), cognitive bridging (g = 1.06, 95% CI [0.84, 1.28], k = 30, p < 0.001), and ontological category shift (g = 0.88, 95% CI [0.50, 1.26], k = 9, p < 0.001). However, a wide-ranging prediction interval [0.19, 2.38] points out a high level of heterogeneity in the distribution of effect sizes. Thus, we investigated the moderating effects of several variables using simple and multiple meta-regression. The final meta-regression model we created explained 35% of overall heterogeneity. This meta-analysis provides robust evidence that conceptual change strategies significantly enhance students' learning in science.

Profound equity and justice-related challenges persist in promoting community engagement with science. The intersecting effects of multiple pandemics—racial and economic injustice, COVID-19, gun violence, and climate change, among others—have all shaped when, how and why people engage with, or even have access to, science. There is also a growing public distrust in science, with broad-reaching implications. The antivaccination movement, one manifestation of the distrust of science, has substantively shaped the course of the COVID-19 pandemic (Tsipursky, 2018). From “alternative facts” to climate change denial, there is increasing public rhetoric, driven by corporate and political interests, that any empirical position can be denied because it does not fit with one's wishes or desires.

In the face of inequitable access to science, distrust, and debate on what can even be considered verifiable information, many look to science education to rescue society from this destructive spiral. Surely, we just need to find better ways of engaging people in science? Yet, the culture and practice of dominant science has been used to justify racism, and to position particular ways of knowing, doing, and being as outside the realm of science. By “dominant science,” we mean the particular forms of Western science that have become dominant to the point that “other ways of knowing, doing, and being are deemed illegitimate or are erased” (Liboiron, 2021; p. 21). The historical lack of inclusion of multiple voices and perspectives in decision-making around scientific issues and in the production of scientific understandings, a lack of transparency of how science is done, including insights into who controls the agenda, whose knowledge counts, and who benefits, all shape how and why communities may—or may not—engage in science. Consequently, a significant divide exists between the scientific community and many members of local communities. Among these tensions emerges the notion of community-driven science.

Consider Flint, MI, a city home to primarily African American families, where 40% of residents live in poverty. In 2014, residents of the city began complaining of discolored and foul smelling and tasting water. However, the city and state were slow to respond. It took a resident-organized effort in collaboration with outside researchers at Virginia Tech University to document what was to become known as one of the “most significant” environmental injustice events of “recent history” (Pauli, 2019). They documented low levels of chlorine in the city's water that led to high levels of the bacteria that causes Legionnaires' disease, and the heavy metals leaching into the water supply at levels in violation of the Safe Drinking Water Act (Zahran et al., 2020), lead to highly elevated levels of lead in children's blood. All of this resulted from the entanglement of economic, political, and structural ineq

Social, political, and cultural complexities observed in environmental justice (EJ) communities require new forms of investigation, science teaching, and communication. Defined broadly, participatory approaches can challenge and change inequity and mistrust in science. Here, we describe Project Harvest and the partnership building and co-generation of knowledge alongside four EJ communities in Arizona. From 2017 to 2021, Project Harvest centered learning around these communities and the participant experience drove the data sharing practice. The framework of sense-making is used to analyze how community scientists (CS) are learning within the context of environmental pollution and (in)justice. The environmental health literacy (EHL) framework is applied to document the acquisition of skills that enable protective decision-making and the capacity of CS to move along the EHL continuum. Using data from surveys, focus groups, and semi-structured interviews, we are asking how did: (1) Personal connections and local relevancy fuel sense-making? (2) Data sharing make pollution visible and connect to historical knowledge to either reinforce or modify their existing mental map around pollution? and (3) The co-creation process build data literacy and a relationship science? Results indicate that due to the program framing, CS personally connected with, and made sense of their data based on use and experience. CS synthesized and connected their pollution history and lived experiences with their data and evaluated contaminant transport. CS saw themselves as part of the process, are taking what they learned and the evidence they helped produce to adopt protective environmental health measures and are applying these skills to new contexts. Here, co-created science nurtured a new/renewed relationship with science. This science culture rooted in co-creation, fosters action, trust, and supports ongoing science engagement. The science learning that stems from co-created efforts can set the pace for social transformation and provide the foundation for structural change.

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.