Reaction mechanisms are central to organic chemistry and organic chemistry education. Assessing understanding of reaction mechanisms can be evaluated holistically, wherein the entire mechanism is considered; however, we assert that such an evaluation does not account for how learners variably understand mechanistic components (e.g., nucleophile, electrophile) or steps (e.g., nucleophilic attack, proton transfer). For example, a learner may have proficiency of proton transfer steps without sufficient proficiency of a step where a nucleophile and electrophile interact. Herein, we report the development of a generalized rubric to assess the level of explanation sophistication for nucleophiles in written explanations of organic chemistry reaction mechanisms from postsecondary courses. This rubric operationalizes and applies chemistry education research findings by articulating four hierarchical levels of explanation sophistication: absent, descriptive, foundational, and complex. We provide evidence for the utility of the rubric in an assortment of contexts: (a) stages of an organic chemistry course (i.e., first or second semester), (b) across nucleophile and reaction types, and (c) across prompt variations. We, as well, present a case study detailing how this rubric could be applied in a course to collect assessment data to inform learning and instruction. Our results demonstrate the practical implementation of this rubric to assess understanding of nucleophiles and offer avenues for establishing rubrics for additional mechanistic components, and understanding and evaluating curricula.

Background Active learning used in science, technology, engineering, and mathematics (STEM) courses has been shown to improve student outcomes. Nevertheless, traditional lecture-orientated approaches endure in these courses. The implementation of teaching practices is a result of many interrelated factors including disciplinary norms, classroom context, and beliefs about learning. Although factors influencing uptake of active learning are known, no study to date has had the statistical power to empirically test the relative association of these factors with active learning when considered collectively. Prior studies have been limited to a single or small number of evaluated factors; in addition, such studies did not capture the nested nature of institutional contexts. We present the results of a multi-institution, large-scale (N = 2382 instructors; N = 1405 departments; N = 749 institutions) survey-based study in the United States to evaluate 17 malleable factors (i.e., influenceable and changeable) that are associated with the amount of time an instructor spends lecturing, a proxy for implementation of active learning strategies, in introductory postsecondary chemistry, mathematics, and physics courses. Results Regression analyses, using multilevel modeling to account for the nested nature of the data, indicate several evaluated contextual factors, personal factors, and teacher thinking factors were significantly associated with percent of class time lecturing when controlling for other factors used in this study. Quantitative results corroborate prior research in indicating that large class sizes are associated with increased percent time lecturing. Other contextual factors (e.g., classroom setup for small group work) and personal contexts (e.g., participation in scholarship of teaching and learning activities) are associated with a decrease in percent time lecturing. Conclusions Given the malleable nature of the factors, we offer tangible implications for instructors and administrators to influence the adoption of more active learning strategies in introductory STEM courses.

A deep understanding of organic chemistry requires a learner to understand many concepts and have fluency with multiple skills. This understanding is particularly necessary for constructing and using mechanisms to explain chemical reactions. Electrophilicity and nucleophilicity are two fundamental concepts to learning and understanding reaction mechanisms. Prior research suggests that learners focus heavily on explicit structural features (e.g., formal charge) rather than implicit features (e.g., an open p-orbital) when identifying and describing the role of electrophiles and nucleophiles in reaction mechanisms; however, these findings come from small-scale, interview-based investigations with a limited number of reaction mechanisms. The work reported herein seeks to further explore the meaning learners ascribe to electrophiles and nucleophiles by evaluating 19 936 written explanations from constructed-response items asking what is happening in reaction mechanisms and why it happens for 85 unique reaction mechanisms across a yearlong postsecondary organic chemistry course. To analyze these data, we developed an electrophile rubric to capture learners’ level of explanation sophistication (Absent, Descriptive, Foundational, and Complex); this electrophile rubric is complementary to a nucleophile rubric previously reported in the literature. Our data show proportional levels of explanation sophistication for electrophiles and nucleophiles (τb = 0.402) across these written explanations of reaction mechanisms. We note that learners’ explanations of nucleophiles tend to be at a higher level than their explanations of electrophiles. While this finding does support prior literature reports, we also found that explanations of mechanisms involving reductions of pi-bonds (e.g., carbonyls) tended to be more sophisticated for electrophiles than for nucleophiles. Overall, our results support the claim that learners are able to discuss both electrophilicity and nucleophilicity; however, learners discuss electrophilicity and nucleophilicity at different levels of sophistication where nucleophilicity predominates for most reaction types.

Acid–base chemistry is a key reaction motif taught in postsecondary organic chemistry courses. More specifically, concepts from the Lewis acid–base model are broadly applicable to understanding mechanistic ideas such as electron density, nucleophilicity, and electrophilicity; thus, the Lewis model is fundamental to explaining an array of reaction mechanisms taught in organic chemistry. Herein, we report the development of a generalized predictive model using machine learning techniques to assess students’ written responses for the correct use of the Lewis acid–base model for a variety (N = 26) of open-ended formative assessment items. These items follow a general framework of prompts that ask: why a compound can act as (i) an acid, (ii) a base, or (iii) both an acid and a base (i.e., amphoteric)? Or, what is happening and why for aqueous proton-transfer reactions and reactions that can only be explained using the Lewis model. Our predictive scoring model was constructed from a large collection of responses (N = 8520) using a machine learning technique, i.e., support vector machine, and subsequently evaluated using a variety of validation procedures resulting in overall 84.5–88.9% accuracies. The predictive model underwent further scrutiny with a set of responses (N = 2162) from different prompts not used in model construction along with a new prompt type: non-aqueous proton-transfer reactions. Model validation with these data achieved 92.7% accuracy. Our results suggest that machine learning techniques can be used to construct generalized predictive models for the evaluation of acid–base reaction mechanisms and their properties. Links to open-access files are provided that allow instructors to conduct their own analyses on written, open-ended formative assessment items to evaluate correct Lewis model use.

Active learning pedagogies are shown to enhance the outcomes of students, particularly in disciplines known for high attrition rates. Despite the demonstrated benefits of active learning, didactic lecture continues to predominate in science, technology, engineering, and mathematics (STEM) courses. Change agents and professional development programs have historically placed emphasis on develop–disseminate efforts for the adoption of research-based instructional strategies (RBIS). With numerous reported barriers and motivators for trying out and adopting active learning, it is unclear to what extent these factors are associated with adoption of RBIS and the effectiveness of change strategies. We present the results of a large-scale, survey-based study of introductory chemistry, mathematics, and physics instructors and their courses in the United States. Herein, we evaluate the association of 17 malleable factors with the tryout and adoption of RBIS. Multilevel logistic regression analyses suggest that several contextual, personal, and teacher thinking factors are associated with different stages of RBIS adoption. These results are also compared with analogous results evaluating the association of these factors with instructors’ time spent lecturing. We offer actionable implications for change agents to provide targeted professional development programming and for institutional leaders to influence the adoption of active learning pedagogies in introductory STEM courses.

The synthesis and structural characterization of molecular complexes of lanthanide iodides supported by the weak-base, diethyl ether, are reported.

To assess the understanding of reaction mechanisms it is necessary to ask learners what the lines, letters, arrows, and other symbols mean. Regurgitation of mechanistic representations is an insufficient measure of learning. In this chapter, we report a framework for assessing understanding through the evaluation of written explanations of what is happening and why for a broad array of reaction mechanisms taught in the postsecondary organic chemistry curriculum. We outline levels of explanation sophistication that can be used to identify opportunities for developing deeper and more meaningful understanding. This work builds on educational research on how organic chemistry students learn and develop expertise in using reaction mechanisms to predict and explain chemical transformations. Purposely considering mechanisms from a mechanistic step or mechanistic component perspective (for example, understanding of a proton transfer) has the potential to spark fresh insights for new and innovative means to facilitate learning. Our long-term goal for this work is to operationalize the framework by applying machine learning techniques to analyze written responses which will provide targeted feedback to educators and learners as they develop understanding of reaction mechanisms.

New hyperbranched polymers have been investigated to provide new structure–function relationships necessary for electrical and optical applications. We can take advantage of long-range delocalization in these structures for energy storage applications. This is accomplished by obtaining high dielectric constants. In this Article, we demonstrate the need for developing high dielectric hyperbranched polymers by first investigating a ceramic/polymer hybrid system and then studying the design criteria for these hyperbranched systems using detailed optical and electronic characterization techniques. Provided in this contribution are the energy storage results with ion-doped polyaniline (PANI) polymers. An enhancement in the dielectric constant emerged from strong long-range polaron delocalization and the mechanism of a hyperelectronic polarization in these polymer systems. A copper phthalocyanine (CuPc) core was selected to build novel hyperbranched polymers to investigate their energy storage and optical properties. We report the results of these hyperbranched polymers, which exhibited high dielectric constants and low dielectric losses. Detailed structure–function relationships were carried out to probe the polaron delocalization mechanism. An outstanding result of a new hyperbranched polymer showed the greatest energy storage capacity of 7.97 J cm–3. These results provide new insights into the design of new organic macromolecules for energy storage.