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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 whatis happening in reaction mechanisms and whyit 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.

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.

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.

Two key topics of many second-year organic chemistry courses are substitution and elimination reactions. Predicting and explaining substitution and elimination reactions tends to be a challenging task for students, as documented in the research literature. Motivated by the many documented challenges experienced by students when learning these reaction types, we developed an adaptive intervention designed to improve student explanations of an SN1 reaction mechanism. The intervention relies on computer-based scoring to evaluate students’ written explanations of a reaction mechanism (i.e., preassessment) in determining which of two tutorials to present to a respondent. Tutorials were designed to facilitate construction of explanations of the given SN1 reaction mechanism based on known student difficulties with reaction mechanisms and recommendations for teaching SN1-related concepts in the chemical education research literature. The Level 1 tutorial aims to move students from a description only explanation to a surface level explanation (i.e., Level 2). The Level 2 Tutorial aims to move students from a surface level explanation to a deeper level explanation (i.e., Level 3). Success of the intervention was evaluated through ordinal logistic regression, finding that the Level 1 Tutorial and the Level 2 Tutorial are net effective, with odds ratios of 2.46 and 1.73, respectively, in increasing students’ level of explanation sophistication.

Assessments that aim to evaluate student understanding of chemical reactions and reaction mechanisms should ask students to construct written or oral explanations of mechanistic representations; students can reproduce pictorial mechanism representations with minimal understanding of the meaning of the representations. Grading such assessments is time-consuming, which is a limitation for use in large-enrollment courses and for timely feedback for students. Lexical analysis and logistic regression techniques can be used to evaluate student written responses in STEM courses. In this study, we use lexical analysis and logistic regression techniques to score a constructed-response item which aims to evaluate student explanations about what is happening in a unimolecular nucleophilic substitution (i.e., SN1) reaction and why. We identify three levels of student explanation sophistication (i.e., descriptive only, surface level why, and deeper why), and qualitatively describe student reasoning about four main aspects of the reaction: leaving group, carbocation, nucleophile and electrophile, and acid–base proton transfer. Responses scored as Level 1 (N= 113, 11%) include only a description of what is happening in the reaction and do not address the why for any of the four aspects. Level 2 responses (N= 549, 53%) describe why the reaction is occurring at a surface level (i.e., using solely explicit features or mentioning implicit features without deeper explanation) for at least one aspect of the reaction. Level 3 responses (N= 379, 36%) explain the why at a deeper level by inferring implicit features from explicit features explained using electronic effects for at least one reaction aspect. We evaluate the predictive accuracy of two binomial logistic regression models for scoring the responses with these levels, achieving 86.9% accuracy (with the testing data set) when compared to human coding. The lexical analysis methodology and emergent scoring framework could be used as a foundation from which to develop scoring models for a broader array of reaction mechanisms.

Both arylsulfonyl and alkylsulfonyl azides can be effectively activated by the cobalt(II) complexes of D2-symmetric chiral amidoporphyrins for enantioselective radical 1,5-C–H amination to stereoselectively construct 5-membered cyclic sulfonamides. In addition to C–H bonds with varied electronic properties, the Co(II)-based metalloradical system features chemoselective amination of allylic C–H bonds and is compatible with heteroaryl groups, producing functionalized 5-membered chiral cyclic sulfonamides in high yields with high enantioselectivities. The unique profile of reactivity and selectivity of the Co(II)-catalyzed C–H amination is attributed to its underlying stepwise radical mechanism, which is supported by several lines of experimental evidence.

Transfer students are a growing population at four-year higher education institutions, especially in STEM degree programs. Though much work has explored the broad transfer student experience, the experience of these students within specific courses is understudied. Furthermore, knowledge of how transfer students approach STEM courses and their affective experience is lacking. We report, herein, on the motivation differences between first-time-in-college students, Florida community college transfer students, and transfer students from non-Florida community college institutions in the first semester of an introductory organic chemistry course. We use the Academic Motivation Scale-Chemistry, an assessment previously used in multiple general chemistry and organic chemistry course contexts including at our study site, to measure motivation. We found a lack of evidence for a difference between motivation for all admission types, even though there are persistent differences between these groups’ course performances. This finding challenges the assumption that transfer students are less or differently motivated than their peers. A goal of reporting this work is to place emphasis on the importance of faculty members knowing the students enrolled in their courses particularly beyond gender or race/ethnicity demographics, exploring the unique learning experiences of students in their courses, and how faculty members play an important role in the success of students in their courses beyond content learning.

The Lewis acid–base model is key to identifying and explaining the formation and breaking of bonds in a large number of reaction mechanisms taught in the sophomore-level year-long organic chemistry course. Understanding the model is, thus, essential to success in organic chemistry coursework. Concept-inventories exist to identify misunderstandings and misconceptions of acid–base theories; open-ended problems, though, have been shown to provide a more nuanced and holistic understanding of how students use acid–base models to explain reactions. The time necessary to score such problems, however, limits their use, especially in large student enrollment courses. Given the efficacy of open-ended problems, there is occasion for the development of methods to efficiently and effectively analyze open-ended assessment responses. In this study, we establish the importance of assessing “use of the Lewis acid–base model to explain a chemical reaction” by determining the association of model use with summative examination performance. In addition, we generate and evaluate a binomial logistic regression model based on lexical analysis techniques for predicting Lewis acid–base model use in explanations of an acid–base proton-transfer reaction. Our work results in a predictive model that can be used to score the open-ended problem used in our study.

The preparation and electropolymerization of a new porphyrin monomer with an amino group connecting the porphyrin core and aniline unit were studied. Cyclic voltammetry, FTIR spectroscopy, and a tandem quadrupole/time of flight mass spectrometry with matrix‐assisted laser desorption/ionization source (MALDI‐QqTOF) were used for the characterization of poly(5‐(4′‐aminophenylamino)‐10,20‐diphenylporphyrin) (PAPADPP). The PAPADPP formed a smooth layer on the Indium tin oxide (ITO) electrode and grew perpendicular to the electrode surface. An oligonucleotide (ODN)‐probe was physisorbed onto the PAPADPP film and tested for hybridization with complementary and noncomplementary ODN segments. Biological recognition was monitored by comparison of the electrochemical signals (cyclic voltammogram) from single and double strand state oligonucleotides. The morphology of the physisorbed ODN‐probe and its hybridization were observed on ITO electrodes using AFM and SEM.

Cobalt carbaporphyrinoids catalyze the cyclopropanation of styrene with a higher trans‐selectivity than the corresponding porphyrin and phthalocyanine complexes.

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