Journal of Research in Science Teaching最新文献

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.

Concept mapping is widely used as a tool for assessing students' understanding of science. To fully realize the diagnostic potential of concept mapping, a scoring method that not only provides an objective and accurate assessment of students' drawn concept maps but also provides a detailed understanding of students' proficiency and deficiencies in knowledge is necessary. However, few of the existing scoring methods focus on the latent constructs (e.g., knowledge, skills, and cognitive processes) that guide the creation of concept maps. Instead, they focus on the completeness of the concept map by assigning a composite score, which makes it difficult to generate targeted diagnostic feedback information for advancing students' learning. To apply the diagnostic classification model to the quantitative analysis of concept maps, this study introduced the novel application of the item expansion-based diagnostic classification analysis (IE-DCA) for this purpose. The IE-DCA can not only assess students' concept mapping abilities along a continuum but also classify students according to their concept mapping attributes when constructing the concept maps. The application and benefits of this approach were illustrated using a physics concept-mapping item related to particle and rigid body. Results showed that the estimated attribute profiles via the IE-DCA provided more detailed information about students' latent constructs than the composite score. Overall, this study illustrates the feasibility and potential of applying IE-DCA to analyze concept maps. Future applications of IE-DCS in other assessments in science education are discussed.

To increase participation of students of color in science graduate programs, research has focused on illuminating student experiences to inform ways to improve them. In biology, Black students are vastly underrepresented, and while religion has been shown to be a particularly important form of cultural wealth for Black students, Christianity is stigmatized in biology. Very few studies have explored the intersection of race/ethnicity and Christianity for Black students in biology where there is high documented tension between religion and science. Since graduate school is important for socialization and Black students are likely to experience stigmatization of their racial and religious identity, it is important to understand their experiences and how we might be able to improve them. Thus, we interviewed 13 Black Christian students enrolled in biology graduate programs and explored their experiences using the theoretical lens of stigmatized identities. Through thematic content analysis, we revealed that students negotiated experiences of cultural isolation, devaluation of intelligence, and acts of bias like other racially minoritized students in science. However, by examining these experiences at the intersection of race/ethnicity and religion, we shed light on interactions students have had with faculty and peers within the biology community that cultivated perceptions of mistrust, conflict, and stigma. Our study also revealed ways in which students' religious/spiritual capital has positively supported their navigation through biology graduate school. These results contribute to a deeper understanding of why Black Christian graduate students are more likely to leave or not pursue advanced degrees in biology with implications for research and practice that help facilitate their success.

This article presents a simple, cognitive theory of science and learning. The first section of the paper develops the theory's two main propositions: (i) A wide range of scientific activities rely heavily on one type of reasoning, hypothetical thinking, and (ii) This type of reasoning is also useful to students for learning science content. The second section of the paper presents a taxonomy of multiple-choice questions that use hypothetical thinking and the third section of the paper tests the theory using data from a college biology course. As expected by the theory, student responses to 24 scientific reasoning questions were consistent with a one-dimensional psychometric construct. Student responses to the scientific reasoning questions explained 36% of the variance in exam grades. Several directions for additional research are identified, including studying the psychometric structure of scientific thinking in more detail, performing randomized, controlled experiments to demonstrate a causal relationship between scientific thinking and learning, and identifying the relative contribution of other factors to success in college.