Journal of Research in Science Teaching最新文献

The role of computation in science is ever-expanding and is enabling scientists to investigate complex phenomena in more powerful ways and tackle previously intractable problems. The growing role of computation has prompted calls to integrate computational thinking (CT) into science instruction in order to more authentically mirror contemporary science practice and to support inclusive engagement in science pathways. In this multimethods study, we present evidence for the Computational Thinking for Science (CT+S) instructional model designed to support broader participation in science, technology, engineering, and mathematics (STEM) pathways by (1) providing opportunities for students to learn CT within the regular school day, in core science classrooms; and (2) by reframing coding as a tool for developing solutions to compelling real-world problems. We present core pedagogical strategies employed in the CT+S instructional model and describe its implementation into two 10-lesson instructional units for middle-school science classrooms. In the first unit, students create computational models of a coral reef ecosystem. In the second unit, students write code to create, analyze, and interpret data visualizations using a large air quality dataset from the United States Environmental Protection Agency to understand, communicate, and evaluate solutions for air quality concerns. In our investigation of the model's implementation through these two units, we found that participating students demonstrated statistically significant advancements in CT, competency beliefs for computation in STEM, and value assigned to computation in STEM. We also examine evidence for how the CT+S model's core pedagogical strategies may be contributing to observed outcomes. We discuss the implications of these findings and propose a testable theory of action for the model that can serve future researchers, evaluators, educators, and instructional designers.

Chemical bonding is central to explaining many phenomena. Research in chemical education and the Framework for K–12 Science Education (the Framework) argue for new approaches to learning chemical bonding grounded in (1) using ideas of the balance of electric forces and energy minimization to explain bond formation, (2) using learning progressions (LPs) grounded in these ideas to support learning, and (3) engaging students in 3D learning reflected in integrating the three dimensions of scientific knowledge to make sense of phenomena. The dimensions include disciplinary core ideas, scientific and engineering practices, and crosscutting concepts. While the Framework describes the theoretical basis of 3D learning, empirical evidence for the development and validation of LPs for 3D learning is limited. This work addresses that issue for the topic of chemical bonding. We develop and validate a 3D construct map for chemical bonding grounded in the idea of balance of electric forces and energy minimization. A construct map represents a finer-grained LP spanning a shorter period and focusing on specific aspects of a larger-scale LP. An NGSS-aligned validated 3D LP has never been reported for the topic of chemical bonding. The LP is based on data from 9th grade Mid-Western and Western students who used the NGSS-aligned curriculum. Multiple validity evidence sources, including interview and item response theory analysis using an assessment tool developed to probe the 3D construct map levels, were used. We demonstrate the feasibility of using the assessment tool for assigning levels to individuals and groups of learners, which is essential for the practical applicability of the 3D construct map and provides teachers with information on how to promote learning. We hope that the 3D LP presented here will serve as a guide to develop instructional and assessment approaches for chemical bonding grounded in the fundamental scientific principles and aligned to NGSS.

Advances in online geospatial technologies (GST) have expanded access to K-12 classrooms which has implications for the support teachers require to effectively integrate GSTs to promote learning. Previous studies have shown the impact of GST-integrated lessons on student engagement, spatial thinking skills, and/or content knowledge; however, most of these studies have been small in scope and scale and frequently focus on the affordances of the technology, without addressing the context of the implementation and student characteristics for whom GST is most impactful. We attempt to address some of these gaps. Our program scaled an effective GST-focused professional learning and development program to a national audience through a facilitator development model. This paper explores the student characteristics and lesson factors that resulted in student interest in science and technology and careers in those fields. After teaching a Geospatial Inquiry lesson created during a teacher workshop, teachers (n = 82) submitted the lessons and surveys on the implementation of Geospatial Inquiry lessons. The implementation surveys and lessons were scored for alignment to the principles of high-quality Geospatial Inquiry. Students (n = 1924) completed a post-lesson retrospective survey and indicated the extent to which their perceptions and attitudes toward science and technology changed because of the lesson. Results indicate that teacher GST performance is associated with increases in student outcomes. Students with previous exposure to science activities were more likely to have increased interest and excitement in science and careers in science but decreased interest in technology careers. Students who had previous exposure to technology activities had increased interest and excitement in technology and careers in technology but decreased interest in science careers. Geospatial Inquiry lessons also had a significant impact on students who are traditionally underrepresented in STEM fields. After participating in the lessons, students who identify as female reported higher engagement and interest in science and higher interest in science careers. Students who identified as Black or Hispanic also reported higher interest and excitement in science and technology, and students who identified as Black reported marginally higher interest in science careers.

Demand for engineering-interested and proficient high school graduates continues to grow across the nation. However, there remains a severe gap in college participation and employment in engineering fields for students with learning disabilities (SWLDs). One potential way to encourage SWLDs to consider engineering as a profession and promote the development of key science attitudes may be through engineering and technology career and technical education (E-CTE) coursework. In this study, we address the following research questions: Do SWLDs take E-CTE courses in the early years of high school at different rates compared to students without learning disabilities? What is the relationship between early E-CTE coursetaking and science attitudes (self-efficacy, utility, identity), and does this differ for students with and without learning disabilities? How do specific engineering career expectations change with respect to enrollment in early E-CTE coursework, and do these differ for students with and without learning disabilities? We utilize the High School Longitudinal Study of 2009 (HSLS) to respond to the research questions through moderation models and a student fixed effects methodology. Ultimately, we found no evidence of SWLD underrepresentation in E-CTE in high school. However, SWLDs were expected to benefit more than the general population from E-CTE participation with respect to higher levels of science self-efficacy and science identity. Implications from these findings include how to encourage persistence along the engineering pathway, the growth of career pathway policies at the state level, and how to incorporate E-CTE practices in academic courses.

Constructing arguments is essential in science subjects like chemistry. For example, students in organic chemistry should learn to argue about the plausibility of competing chemical reactions by including various sources of evidence and justifying the derived information with reasoning. While doing so, students face significant challenges in coherently structuring their arguments and integrating chemical concepts. For this reason, a reliable assessment of students' argumentation is critical. However, as arguments are usually presented in open-ended tasks, scoring assessments manually is resource-consuming and conceptually difficult. To augment human diagnostic capabilities, artificial intelligence techniques such as machine learning or natural language processing offer novel possibilities for an in-depth analysis of students' argumentation. In this study, we extensively evaluated students' written arguments about the plausibility of competing chemical reactions based on a methodological approach called computational grounded theory. By using an unsupervised clustering technique, we sought to evaluate students' argumentation patterns in detail, providing new insights into the modes of reasoning and levels of granularity applied in students' written accounts. Based on this analysis, we developed a holistic 20-category rubric by combining the data-driven clusters with a theory-driven framework to automate the analysis of the identified argumentation patterns. Pre-trained large language models in conjunction with deep neural networks provided almost perfect machine-human score agreement and well-interpretable results, which underpins the potential of the applied state-of-the-art deep learning techniques in analyzing students' argument complexity. The findings demonstrate an approach to combining human and computer-based analysis in uncovering written argumentation.

Science identity, or one's sense of recognition and competence as a scientist, is an invaluable tool for predicting student persistence and success, but is understudied among undergraduates completing preparatory work for later studies in medicine, nursing, and allied health (“pre-health career students”). In the United States, pre-health career students make up approximately half of all biology students and, as professionals, play important roles in caring for an aging, increasingly diverse population, managing the ongoing effects of a pandemic, and navigating socio-political shifts in public attitudes toward science and evidence-based medicine. Pre-health career students are also often members of groups marginalized and minoritized in STEM education, and generally complete their degrees in community college settings, which are chronically under-resourced and understudied. Understanding these students' science identities is thus a matter of social justice and increasingly important to public health in the United States. We examined science identity and engagement among community college biology students using two scales established and validated for use with STEM students attending four-year institutions. Exploratory and confirmatory factor analysis were used on two sub-samples drawn from the pool of 846 participants to confirm that the factor structures functioned as planned among the new population. Science identity values were then compared between pre-health career students (pre-nursing and pre-allied health) and other groups. Pre-health career students generally reported interest and performance/competence on par with their traditional STEM, pre-med, and pre-dentistry peers, challenging popular assumptions about these students' interests and abilities. However, they also reported significantly lower recognition than traditional STEM and pre-med/dentistry students. The implications for public health, researchers, and faculty are discussed.

There is strong agreement in science teacher education of the importance of teachers' content knowledge for teaching (CKT), which includes their subject matter knowledge and their pedagogical content knowledge. However, there are limited instruments that can be easily administered and scored on a large scale to assess and study elementary science teachers' CKT. Such measures would support strategic monitoring of large groups of science teachers' CKT and the investigation of comparative questions about science teachers' CKT longitudinally across the professional continuum or across teacher education or professional development sites. To address this gap, this study focused on designing an automatically scorable summative assessment that can be used to measure preservice elementary teachers' (PSETs') CKT in one high-leverage science content area: matter and its interactions. We conducted a field test of this CKT instrument with 822 PSETs from across the United States and used the response data to examine how this instrument functions as a potential tool for measuring PSETs' CKT in this science content area. Results suggest this instrument is reliable and can be used on large scale to support valid inferences about PSETs' CKT in this content area. In addition, the dimensionality analysis showed that all items measure a single construct of CKT about matter and its interactions, as participants did not show any differential performance by content topic or work of teaching science instructional tool categories. Implications for progressing the field's understanding of the nature of CKT and approaches to developing summative instruments to assess science teachers' CKT are discussed.

This longitudinal study examines the relationship between students' knowledge-in-use performance and their performance on third-party designed summative tests within a coherent and equitable learning environment. Focusing on third-grade students across three consecutive project-based learning (PBL) units aligned with the Next Generation Science Standards (NGSS), the study includes 1067 participants from 23 schools in a Great Lakes state. Two-level hierarchical linear modeling estimates the effects of post-unit assessments on end-of-year summative tests. Results indicate that post-unit assessment performances predict NGSS-aligned summative test performance. Students experiencing more PBL units demonstrate greater gains on the summative test, with predictions not favoring students from diverse backgrounds. This study underscores the importance of coherence, equity, and the PBL approach in promoting knowledge-in-use and science achievement. A systematically coherent PBL environment across multiple units facilitates the development of students' knowledge-in-use, highlighting the significance of designing science and engineering practices (SEPs) and crosscutting concepts coherently and progressively, with intentional revisitation of disciplinary core ideas (DCIs). The study also investigates how the PBL approach fosters equitable learning environments for diverse demographic groups, offering equitable opportunities through equity-oriented design. Contributions include a coherent assessment system that tracks and supports learning aligned with NGSS, emphasizing the predictive power of post-unit assessments, continuous monitoring and tracking. The implications of context similarity and optimal performance expectations within units are discussed. Findings inform educators, administrators, and policymakers about the benefits of NGSS-aligned PBL systems and the need for coherent and equitable learning and assessment systems supporting knowledge-in-use development and equitable opportunities for all learners.

In the past, students' participation in science competitions has been positively associated with their aspirations to pursue a career in science. Previous studies, however, were predominantly focused around successful competitors, overlooking the largest group of participants: those who are faced with early elimination. We therefore aimed to investigate the effects of elimination on the development of biology-related study and career task values and expectancy of success in first-round participants of the German Biology Olympiad (N = 381, mean age 16.5 years, 72% female). This study was the first of its kind to use a latent change score model approach to examine the effects of early elimination, with a particular focus on participants who placed great emphasis on succeeding in the competition. We found that, regardless of success or failure, participants' biology-related study and career task value remained stable from the first to the second round of the competition, while their expectancy of success in biology-related studies and career developed positively. Yet, for those participants who placed great importance on advancing in the competition, early elimination interfered with the development of study and career expectations, resulting in a weaker development. The outcomes of this study suggest that (1) science competitions should re-envision themselves to more directly address participants' values about studies and careers, especially in earlier competition rounds, and (2) science competitions should find innovative ways to provide detailed feedback to students and teachers to improve post-elimination performance. Our findings complement existing expectancy-value research and can serve as a starting point for future studies exploring mechanisms behind early elimination in different science domains and cultural contexts, providing empirical insight into creating an inclusive and supportive environment for all science competition competitors.

Computational thinking (CT) is vital for success in numerous domains. However, the nature, definition, and scope of CT are ill-defined, and research on how best to develop CT is very limited. This study focused on how thinking styles and STEM attitudes have effects on computational thinking. Using a proportionate stratified random sampling procedure, 1195 students from two universities were surveyed. A structural equation modeling analysis showed that students' thinking styles and STEM attitudes directly predicted their computational thinking skills and that thinking styles mediated the relationship between STEM attitudes and computational thinking skills. Thinking styles and STEM attitudes are strong predictors of CT skills. Based on the results, we recommended that the conceptualization of CT be broadened to reflect its trans-disciplinary nature within the context of STEM education. This study adds to the limited theoretical understanding of CT and CT-predictors in higher education, which has been studied much less than in K-12 education.