Thinking and Learning in Nested Systems: The Classroom Level.

Teaching and learning in college chemistry classrooms is affected by a variety of structural and psychosocial factors that influence classroom dynamics. In this second part of a two-part perspective [Talanquer et al. J. Chem. Educ.10.1021/acs.jchemed.3c00838], we review and discuss the results from research that has helped us understand the complex social and knowledge dynamics that emerge in interactive learning environments. We use this analysis to make explicit major insights about curriculum, instruction, assessment, teachers, and students gained in the past 25 years and to summarize their implications for chemistry education.

Small Group Conversations in a POGIL-Based Class: How English Learners Engage in a Joint Knowledge Construction Process to Reach a Shared Understanding.

Collaboration is an aspect of engagement that focuses on learning through group work and having discussions with other learners. Active learning approaches are a way to foster collaborative engagement because they provide more opportunities for interaction among learners. Process Oriented Guided Inquiry Learning (POGIL), a socially mediated active learning approach, uses verbalizing and discussing ideas with peers in small groups to reach a shared understanding. Due to the growing number of immigrants in the United States, the number of English Learners (ELs) in American classrooms has been increasing rapidly. ELs encounter challenges such as unfamiliarity with American science class norms and expectations, feelings of not being valued and socially accepted, and instructors' lower expectations of them. These challenges can impact this group of students' learning and achievements. Previous studies have discussed that learning chemistry can be more challenging for EL students due to the critical role of language in learning. We argue that ELs use discourse moves differently compared to non-ELs in a POGIL-based class in terms of engaging in conversations that can lead to a shared understanding at the group level through a joint knowledge construction process. Our findings indicated that, in our sample, ELs were less likely to engage in discursive moves than non-ELs. This difference may result in missed opportunities for a shared understanding and joint knowledge construction. In addition to differences between EL and non-EL students in our samples, we also found differences between EL students who attended K-12 schools in the United States compared to international EL students. Implications for future studies of these possibly distinct EL populations are considered.

A Longitudinal Study Identifying the Characteristics and Content Knowledge of Those Seeking Certification to Teach Secondary Biology in the United States.

Teacher content knowledge has been identified as a key prerequisite to effective instruction, and current educational policies require measurement of teacher content knowledge to assess candidacy for licensure. The primary instruments used in the United States are the Praxis Subject Assessment exams, which are designed to measure the subject-specific content knowledge needed to be a teacher. The Praxis Biology Subject Assessment exam, used by 42 U.S. states in the past decade, is the most common national measure used to determine biology content knowledge for teacher certification. Demographic and performance data from examinees (N = 43,798) who took the Praxis Biology Subject Assessment from 2006 to 2015 were compared to present a much-needed picture of who is seeking certification to teach biology, how different groups of aspiring biology teachers have performed, and how demographic makeup of prospective biology teachers compares with reports in previous studies describing the composition of the biology teacher workforce. Results indicate the majority of students self-reported as White (76%), female (66%), having undergraduate grade point averages (GPAs) at or above a 3.0 (76%) and majoring in biology (45%). Additionally, the demographic data were included in a linear regression model to determine the factors that explained the most variance in performance of the examinee. The model revealed substantial differences in average performance and pass rates between examinees of different genders, races, undergraduate majors, undergraduate GPAs, and census regions. This suggests that if the examinee is a White science, technology, engineering, and mathematics major, man with a 3.5 or higher undergraduate GPA, resides in the western United States, or plans to teach in a suburban school, the examinee will on average outperform their counterparts on the exam. From our analyses, we suggest several measures for the improvement of the biology teaching workforce and establish potential issues in the teacher pipeline that may impact the quality and diversity of U.S. biology teachers.

GA4GH Passport standard for digital identity and access permissions.

The Global Alliance for Genomics and Health (GA4GH) supports international standards that enable a federated data sharing model for the research community while respecting data security, ethical and regulatory frameworks, and data authorization and access processes for sensitive data. The GA4GH Passport standard (Passport) defines a machine-readable digital identity that conveys roles and data access permissions (called "visas") for individual users. Visas are issued by data stewards, including data access committees (DACs) working with public databases, the entities responsible for the quality, integrity, and access arrangements for the datasets in the management of human biomedical data. Passports streamline management of data access rights across data systems by using visas that present a data user's digital identity and permissions across organizations, tools, environments, and services. We describe real-world implementations of the GA4GH Passport standard in use cases from ELIXIR Europe, National Institutes of Health, and the Autism Sharing Initiative. These implementations demonstrate that the Passport standard has provided transparent mechanisms for establishing permissions and authorizing data access across platforms.

The Data Use Ontology to streamline responsible access to human biomedical datasets.

Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset's allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers' discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide.

Diagnosing the current state of out-of-field teaching in high school science and mathematics.

The U.S. government has acknowledged the critical role that teachers play in the production of Science, Technology, Engineering, and Mathematics (STEM) professionals who will drive our nation's economy. The No Child Left Behind Act of 2001 (NCLB) was passed to improve the quality of education nationwide, in part, by decreasing the number of out-of-field (OOF) teachers. However, the impact of NCLB and related efforts on the current state of OOF teaching in high school science and mathematics has yet to be examined. Our analysis of data from the National Teacher and Principal Survey (NTPS) indicates that from 2003-2016, the proportion of OOF teachers in chemistry and physics has increased, and there has been an increase in the number of students assigned to OOF teachers across subjects. We discuss the societal impact of our results and the critical role that policymakers, school administrators, and academic institutions, particularly university faculty, can play in its solution.

Social network data from teacher leader development.

Social network analysis can draw upon surveys and discussions to generate quantitative and qualitative data. We describe network data generated via a social network survey and discussion activity with high school science teachers in a teacher leadership development program. Data include social network maps related to seeking expertise in teaching content and/or pedagogy, disaggregated by contacts at the school, district, state, nation, and international spheres of influence. Data also include transcripts of the activity and teacher discussions of networks in their own educational settings. This data article is related to the research article, "The use of visual network scales in teacher leader development" Polizzi et al., 2019, where data interpretation can be found.

Evaluation of a Noyce program: Development of teacher leaders in STEM education.

The development of teacher leaders in science, technology, engineering, and mathematics has become a focus as demand grows on the national scale to improve student learning in these disciplines. As teachers' role in leadership continues to be redefined, research and professional development in teacher leadership will continue to evolve. Given the lack of a clear conceptualization of teacher leadership in the empirical literature, there is a clear methodological challenge for evaluators who are charged with assessing the impact of teacher leadership professional development programs. This paper describes how both the Utilization-Focused Evaluation and Theory-Driven Evaluation frameworks were used concurrently to design evaluation methods that were effective for assessing the impact of a dynamic teacher leadership program. The evaluation is specifically situated within the context of a Robert Noyce Scholarship Program, which aimed to grow veteran science teachers into teacher leaders. The paper describes how the evaluation frameworks used guided the evaluation methods, provides illustrative evaluation results, and states lessons learned from the author's experiences working within this context. This paper aims to provide an example of evaluation methods that could be replicated by evaluators' working within a Noyce or teacher leadership context.

Responsible sharing of biomedical data and biospecimens via the "Automatable Discovery and Access Matrix" (ADA-M).

Given the data-rich nature of modern biomedical research, there is a pressing need for a systematic, structured, computer-readable way to capture, communicate, and manage sharing rules that apply to biomedical resources. This is essential for responsible recording, versioning, communication, querying, and actioning of resource sharing plans. However, lack of a common "information model" for rules and conditions that govern the sharing of materials, methods, software, data, and knowledge creates a fundamental barrier. Without this, it can be virtually impossible for Research Ethics Committees (RECs), Institutional Review Boards (IRBs), Data Access Committees (DACs), biobanks, and end users to confidently track, manage, and interpret applicable legal and ethical requirements. This raises costs and burdens of data stewardship and decreases efficient and responsible access to data, biospecimens, and other resources. To address this, the GA4GH and IRDiRC organizations sponsored the creation of the Automatable Discovery and Access Matrix (ADA-M, read simply as "Adam"). ADA-M is a comprehensive information model that provides the basis for producing structured metadata "Profiles" of regulatory conditions, thereby enabling efficient application of those conditions across regulatory spheres. Widespread use of ADA-M will aid researchers in globally searching and prescreening potential data and/or biospecimen resources for compatibility with their research plans in a responsible and efficient manner, increasing likelihood of timely DAC approvals while also significantly reducing time and effort DACs, RECs, and IRBs spend evaluating resource requests and research proposals. Extensive online documentation, software support, video guides, and an Application Programming Interface (API) for ADA-M have been made available.

Guest control of a hydrogen bond-catalysed molecular rotor.

Herein, the control of a molecular rotor using hydrogen bonding guests is demonstrated. With a properly positioned phenol substituent, the N-arylimide rotors can form an intramolecular hydrogen bond that catalyses the rotational isomerization process. The addition of the guests disrupts the hydrogen bond and raises the rotational barrier, slowing the rotation by two orders of magnitude.

Setting a Standard for Chemistry Education in the Next Generation: A Retrosynthetic Analysis.

A diverse and highly qualified chemistry teaching workforce is critical for preparing equally diverse, qualified STEM professionals. Here, we analyze National Center for Education Statistics (NCES) Schools and Staffing Survey (SASS) data to provide a demographic comparison of the U.S. secondary chemistry teaching population in high-needs and non-high-needs public schools as well as private schools during the 2011-2012 academic year. Our analysis reveals that the chemistry teaching workforce is predominantly white and significantly lacks in-field degrees or certification across school types, though high-needs and private schools are most affected by this lack of teacher qualification. Given these results, we attempt to retrosynthetically identify the pathway yielding a qualified chemistry teaching workforce to draw attention to the various steps in this scheme where reform efforts on the part of individual faculty, academic institutions, and organizations can be concentrated.

Carbohydrate recognition by porphyrin-based molecularly imprinted polymers.

[structure: see text] Porphyrin-based molecularly imprinted polymers (MIPs) were prepared for carbohydrate recognition. A urea-appended porphyrin functional monomer was utilized to provide complementary functionality and quality binding sites throughout the polymer. Each porphyrin-based polymer demonstrates high affinity and differential selectivity for closely related carbohydrates that correlate to the structure of the template used in the imprinting process.

Characterization of the heterogeneous binding site affinity distributions in molecularly imprinted polymers.

Molecularly imprinted polymers (MIPs) are polymers that can be tailored with affinity and selectivity for a molecule of interest. Offsetting the low cost and ease of preparation of MIPs is the presence of binding sites that vary widely in affinity and selectivity. Presented is a review of methods that take into account binding site heterogeneity when calculating the binding properties of MIPs. These include the bi-Langmuir, Freundlich, and Langmuir-Freundlich binding models. These methods yield a measure of heterogeneity in the form of binding site affinity distributions and the heterogeneity index. Recent developments have made these methods surprisingly easy to use while also yielding more accurate measures of the binding properties of MIPs. These have allowed for easier comparison and optimization of MIPs. Heterogeneous binding models have also led to a better understanding of the imprinting process and of the advantages and limitations of MIPs in chromatographic and sensor applications.

Characterization of the imprint effect and the influence of imprinting conditions on affinity, capacity, and heterogeneity in molecularly imprinted polymers using the Freundlich isotherm-affinity distribution analysis.

Molecularly imprinted polymers (MIPs) have been used in a wide range of analytical applications in particular in chromatography and sensing. However, the binding properties in MIPs are typically measured only in a narrow concentration range, which corresponds to only a subset of the sites in MIPs. This limited analytical window and binding site heterogeneity of MIPs leads to inaccuracies and inconsistencies in the estimation of their binding properties. This has hampered the characterization and optimization of MIPs for analytical applications. In this study, the origins of the molecular imprinting effect were studied using the newly developed Freundlich isotherm-affinity distribution (FIAD) analysis. The analysis is able to readily calculate an affinity distribution for MIPs from the limited analytical window. The FIAD analysis also yields an estimate of number, affinity, and heterogeneity for this subset of binding sites. Consistent with previous studies, MIPs were found to have higher capacities than the corresponding nonimprinted polymers (NIPs). Interestingly, MIPs were also found to be more heterogeneous than NIPs. Examination of variables in the imprinting process including temperature, template concentration, and cross-linking percentages further confirmed these trends. Based on these observations, a model for the imprinting effect was developed. The larger population of high-affinity sites in MIPs appears to arise from a broadening of the heterogeneous distribution. This suggests that noncovalent MIPs may be ill-suited for chromatographic applications and other applications that are detrimentally affected by binding site heterogeneity and better suited to applications that are less affected by heterogeneity such as sensing.