How Are Students Solving Familiar and Unfamiliar Organic Chemistry

Article Cite This: J. Chem. Educ. XXXX, XXX, XXX−XXX

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How Are Students Solving Familiar and Unfamiliar Organic Chemistry Mechanism Questions in a New Curriculum? Declan M. Webber and Alison B. Flynn* Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario, Canada K1N 6N5

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S Supporting Information *

ABSTRACT: In this study, we continue our efforts to address students’ difficulties understanding organic chemistry, particularly in connecting structure to function and using the language of chemistry to explain how and why reactions occur. For the first time, we have characterized students’ work and problem-solving strategies on familiar and unfamiliar mechanism questions in a new course format: a patterns of mechanisms organic chemistry curriculum and flipped course format. This study began with analysis of students’ exam answers then more deeply explored the features to which they attend and their strategies using a think-aloud interview protocol. Success rates were higher on both the familiar and unfamiliar mechanism questions in the new course format with medium effect sizes, although we cannot conclude a causal link with the new format. Interview analysis revealed that all used the electron-pushing formalism correctly and as an initial and routine part of their problemsolving strategy. Most participants regularly used concepts of nucleophiles, electrophiles, electronegativity, and charges (full and partial) in their problem-solving process. Participants used dipoles and charges (full and partial) to reveal nucleophilic and electrophilic portions of molecules, often expanding structures to do so. In difficult questions or steps, successful strategies involved expanding and mapping in combination with chemistry reasoning. Every participant struggled with an acronym in one question, LDA, wanting to draw its structure and identify its role but not being sure they could remember it. That difficulty drawing the structure became a barrier to analyzing the reactivity. To emphasize the importance of connecting structure and reactivity over memorizing, we recommend drawing out structures and not only acronyms on assessments. In this article, we describe the context, methods, findings, and implications for research and instruction. KEYWORDS: Upper-Division Undergraduate, Second-Year Undergraduate, Organic Chemistry, Constructivism, Mechanisms of Reactions, Chemical Education Research FEATURE: Chemical Education Research

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face in their organic chemistry courses,2 especially surrounding mechanistic reasoning.3 Students have struggled to understand the purpose of reaction mechanisms and the associated electronpushing formalism (EPF),4−14 the concepts of nucleophiles and electrophiles,15,16 and core ideas such as acid−base chemistry.17−19 Part of these difficulties could stem from the level of abstraction needed when solving organic reaction mechanism problems20−22 and students’ difficulties in seeing patterns through seemingly dissimilar surface features.23−25 Despite

INTRODUCTION Our goal is to help students develop the chemistry knowledge, skills, and values to become proficient scientists, decision makers, and members of the public so that our graduates can leverage their chemistry knowledge to address the issues we face. Many global issues are highlighted by the UN’s Sustainable Development Goals,1 in which chemistry plays a central role. In our organic chemistry courses, a major goal is helping students better develop skills in connecting structure to reactivity through learning patterns and principles. In doing so, students would ideally be better positioned to interpret, predict, and explain reactivity in new contexts. Despite the need for organic chemistry knowledge, research has demonstrated many struggles that thousands of students © XXXX American Chemical Society and Division of Chemical Education, Inc.

Received: March 2, 2018 Revised: June 6, 2018

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DOI: 10.1021/acs.jchemed.8b00158 J. Chem. Educ. XXXX, XXX, XXX−XXX

Journal of Chemical Education

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Figure 1. Overview of the uOttawa curriculum.

Figure 2. Connecting structural knowledge to reactivity includes three major areas: organic chemistry’s language (e.g., the electron-pushing formalism (EPF)), how reactions occur (process and patterns), and why reactions occur (causal mechanistic reasoning).

bond-forming and bond-breaking reactions, and sought to justify the reasons for reactions even when not asked to do so.30 When examining how students categorize organic reactions, we identified four key levels (in addition to “unknown”): organizing by structural features, chemical properties, reaction types, and reaction mechanisms, with the former two being static and the latter two being dynamic ways of categorizing reactions.24 Professors and Ph.D. students had more cohesive ways of organizing reactions than undergraduate and Master’s students and did so exclusively based on reaction types and mechanisms.25 In the present study, we report students’ approaches to solving familiar and unfamiliar organic chemistry questions (i.e., propose a product, propose a mechanism), including success rates, features to which students attended, and problem-solving strategies.

students’ difficulties, the mechanistic thinking that is central to experts’ work also seems to have a benefit for students when problem solving.26 In January 2012, the University of Ottawa (uOttawa) implemented a new organic chemistry curriculum organized by patterns of mechanisms (Figure 1).27 In the new curriculum, students are taught the EPF (organic chemistry’s language) before learning specific reactions.27 By doing so, students should in principle have a greater fluency in organic chemistry’s language before they learn reactions. As they begin to learn reactions, their language fluency should allow for a lower cognitive load and potential for greater depth of learning. The reactivity portion begins with acid−base chemistry28 and is then organized by patterns of mechanism and taught with an increasing gradient of difficulty. Our work studying student learning in the new curriculum has been focused on the reactivity portion of the curriculum (Figure 2). We have seen a great deal of student success interpreting organic chemistry’s language (i.e., the EPF)seeing few reversed arrows, pentavalent carbon atoms, etc.although students have had difficulty with more complicated questions that include intramolecular reactions, implicit atoms, and rearrangements (arguably beyond the introductory organic chemistry levels).29 When we studied students’ meaning-making while they used organic chemistry’s language, we found that students overwhelmingly used process-oriented thinking (rather than product-oriented thinking), examining electrons involved in

Research Questions

In this study, we investigated the following research questions related to Organic Chemistry I and II: 1. How do organic chemistry students’ success rates compare on mechanistic exam questions before and after the course format change? 2. What are the features to which students attend when asked to propose a product or explain how a product forms? 3. What strategies and errors are evident when students approach familiar and unfamiliar mechanistic questions? B



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METHODS

We explored the strategies used and errors made in the answers to the same questions by coding and analyzing the differences between years and also between successful and unsuccessful answers. Think-Aloud Interviews. Participants were recruited by e-mail and online course announcement from the Organic Chemistry II course in 2015. The 11 participants were offered Starbucks gift cards as compensation. Nine of the participants were second-year students for whom the course was compulsory to their program. Of the remaining two students, one was in a biology program and the other in a psychology program. Pseudonyms are used to protect participants’ identities. Interviews were conducted over a 10 day period in February 2016, approximately 2 months after the course concluded; participants were not taking an organic chemistry course at the time of the interviews. Think-aloud interviews are used in research when studying “individual differences in performing the same task”.34 By listening to the students’ thought processes, we were able to better observe the features on which focused students and the processes they used to solve each problem.35 Think-aloud interviews provided us a more complete picture of how students approach mechanistic problems because we were not limited to what the student wrote down. Many students may attend to critical features that they do not write down on the exam, and they may use problem-solving strategies that cannot be seen on paper (e.g., mapping the question in their head). During the interviews, the participants were provided with four problems and were asked to solve them as they would on an exam (Figure 3). The interviews were conducted using a Livescribe Echo smartpen36 and lasted 30−45 min. The questions were given to the participants one at a time. The first question served as a warm-up question to help the participant feel comfortable with the think-aloud interview process. Questions 2, 3, and 4 were used because they were the same questions that were studied in the exam analysis phase. For question 3 (an unfamiliar question), we had two independent prompts prepared in case the student was completely stuck. In the first prompt, the ester compound was expanded for the participant (Figure 3). If the participant was still struggling after receiving the first prompt, a second prompt was provided, which contained the first intermediate of the reaction. These prompts had two goals. The first goal was to determine whether they made a difference in the participant’s ability to solve the problem. Second, they could help to us identify certain aspects of the problem that may act as barriers to some students’ problem-solving ability. Methodological FrameworkGrounded Theory: Systematic Design. The interview transcripts initially provided a large quantity of data, not all of which were directly relevant to research questions addressed in this study. Therefore, in order to thoroughly and effectively analyze the data, a hierarchical coding process was used. Systematic design in grounded theory consists of three stages of coding; open coding, axial coding, and selective coding.37 In the open-coding stage, we coded virtually anything that the participant used as a technique, strategy, thought process, or action when approaching the mechanistic problems. In other words, we coded any feature of the question to which the participant appeared to attend (e.g., leaving groups, nucleophiles, electron density, etc.). In the axial-coding stage, we established the “core category” of the data.38 We determined the core category of this study to be the critical features to which students attend when faced with a mechanistic problem.

Courses and Instructional Context

Our research focused on the first two organic chemistry courses students take at uOttawa. Organic Chemistry I is offered in the winter semester of first-year undergraduate studies. Organic Chemistry II is offered in the fall semester after the students have completed Organic Chemistry I. Both courses are 12 weeks long and include 3 hours of class and a 1.5 hour discussion group each week. Organic Chemistry I also includes a laboratory component, which is 3 hours long and runs biweekly. In 2012, the institution implemented a patterns of mechanisms organic chemistry curriculum.27 The topics taught up until the end of Organic Chemistry II include structure, properties, stereochemistry, and conformational analysis of organic compounds; IR and 1H NMR spectroscopy; electron-pushing formalism/symbolism; acid−base chemistry; π-bond electrophiles (e.g., 1,2-carbonyl addition reactions, acetals and imine formation, addition−elimination reactions, etc.); π-bond nucleophiles; aromaticity and electrophilic aromatic substitution; SN1, SN2, E1, and E2 mechanisms; and α-carbons as nucleophiles (e.g., aldol condensation, alkylation).31 Beginning in Fall 2013, these courses were both taught using a flipped format. In this flipped format, students received the “lecture” material as online videos and notes that they were expected to watch and read before coming to class. Class time was instead used for interactive problem-solving activities in order to help students achieve the intended learning outcomes.32 Data Sources

Exam Questions and Analysis. We began by analyzing Organic Chemistry I final exams from 2012 and Organic Chemistry II final exams from 2011, 2013, and 2015. We selected questions that had been asked in all years, had never been released to students, and were either familiar or unfamiliar to students. We analyzed a total of three exam questions (Q2− Q4). The questions are shown in Figure 3 as they appeared on the exam, and the anticipated answers can be found in the Supporting Information (SI). We used the same questions in the interview portion as in the exam, with an additional question (Q1) intended as a warm-up. Data from 2011 are described as being from the old course format, while 2012, 2013, and 2015 include data from the new course format. We defined a familiar question as one with a reaction mechanism that had been explicitly taught (e.g., Grignard reaction, enolate alkylation) and an unfamiliar question as one with a reaction mechanism that had not been explicitly taught but that students could solve by integrating a number of mechanistic steps they had previously learned. The distribution and numbers of participants from this part of the study can be found in Table 1. The institution’s Office of Research Ethics and Integrity (i.e., Institutional Review Board) approved all phases of this study. We compared students’ mechanism question exam scores on familiar and unfamiliar questions before and after the curriculum change. Because the data were not normally distributed, we performed nonparametric tests to determine whether the scores had statistically significant differences using the independentsamples Krusksal−Wallis test for the familiar question (three groups) and the independent-samples Mann−Whitney U test for the unfamiliar question (two groups). We then used Cohen’s d effect-size tests to estimate the magnitude of this difference.33 We also compared students’ grade-point averages (GPAs) in each group using analysis of variance (ANOVA). C



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Figure 3. Exam and interview questions analyzed, shown with the instructions given on the exam and in the interview.

In the selective-coding stage, a theory was established on the basis of interrelationships between the categories (i.e., the critical features to which participants attended), and we later used this theory to probe our research questions.37 In this study, our goal was to analyze the various strategies that students use when faced with mechanistic problems. By analyzing the features to which students attend and the features they determine to be critical, we identified strategies that students employed (RQ3). We expanded our coding paradigm to include the strategies that emerged from our interview data. Through further analysis of the strategies used by the interviewed participants, we determined which strategiesor combinations of strategiesseemed associated with success on these mechanistic problems (RQ2).

FINDINGS: EXAM ANALYSIS (RQ1)

Mechanism-Users Scored Higher on Q2 than Non-Mechanism-Users

Q2 asked students to provide the product of an unfamiliar reaction but did not require the use of a mechanism; the question indicated that part marks were possible for students who provided plausible mechanisms but gave incorrect answers. Of the 300 answers, 63% of the students chose to draw a mechanism in their answers. The average score of mechanismusers was 30% higher than those of non-mechanism-users, with corresponding median scores of 2 and 0 out of 3, respectively (Figure 4); this difference was significant (t(298) = 7.04, D



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Scores on the Familiar Mechanistic Problem Were Higher in the New Format

Table 1. Distribution of Data Sources for Exam Analysis

Question

Organic Chemistry Course

Q2 Q3 Q3 Q4 Q4 Q4

I II II II II II

Year

Number of Participants

Before or After the Format Change

Familiar or Unfamiliar Question

2012 2011 2013 2011 2013 2015

300 200 200 216 232 240

After Before After Before After After

Unfamiliar Unfamiliar Unfamiliar Familiar Familiar Familiar

Students taught in the new format had higher scores on familiar Q4 than students taught before the format change (Figure 5). The average scores on the familiar question in the new format (86% and 81%) were higher than the average score in the old format (76%) (H(2) = 10.98, p = 0.004), with medium effect sizes (Cohen’s d = 0.33 and 0.17 for 2013 and 2015 compared with 2011, respectively). There was no statistically significant difference between the cohorts’ GPAs on the basis of one-way ANOVA (F(2, 1164) = 0.348, p = 0.706). These results suggested that students taught using the new format were generally more capable of solving familiar mechanism questions than students taught in the traditional

p < 0.0001). These findings led us to add this question to the interview guide so that we could more deeply explore how students approached this question.

Figure 4. Distribution of unfamiliar Q2 scores (out of 3) between mechanism-users and non-mechanism-users.

Figure 5. Distribution of familiar Q4 scores (out of 5) between the old and new course formats. E



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Figure 6. Distribution of unfamiliar Q3 scores (out of 5) between the old and new formats.

Figure 7. Comparison of final exam scores and scores on the familiar question: (A) old format (2011, N = 216); (B) new format, year 2 (2013, N = 232); (C) new format, year 4 (2015, N = 240).

Comparison of Individual Question Scores to Overall Final Exam Grades

format. In principle, the new format provided students with a deeper understanding of the patterns and core concepts that govern reaction mechanisms and opportunities for practice and feedback, which allowed them to perform better on a question that they had never seen specifically but that involved a familiar mechanism.

We explored the relationship between the question and exam scores to see whether there was an association between how students performed on these questions and their overall success in the organic chemistry course (Figure 7). In all three years (old and new curricula), there was a medium correlation between the final exam scores and the familiar question scores (R2 = 0.42, 0.31, and 0.40). Students who achieved 5 out of 5 on the familiar problem had final exam grades distributed between 35% and 100% with a medium correlation. The high median scores on this question compared with the final exam scores suggest that even students with fragmented knowledge could propose a plausible mechanism. Every student who achieved a score of 0 or 1 out of 5 on this familiar question scored below 60% on the final exam, with the exception of two students in the new format. There does seem to be a relationship between low scores on this question and low scores on the final exam.

Scores on the Unfamiliar Mechanistic Problem Were Higher in the New Format

One goal of the curriculum change was to help students better predict plausible reaction mechanisms for unfamiliar problems based on principles of reactivity.27 Scores on an unfamiliar problem (Q3) were low in both years (Figure 6). The average score on the unfamiliar question in the new format (56%, N = 200) was higher than the average score in the old format (40%, N = 200) (U = 14,104.5, p < .001 with a medium effect size (Cohen’s d = 0.38)). Based on these findings, the format change may have helped students apply mechanistic thinking to reactions that they had never seen before. However, we cannot conclude a causal relationship based on an analysis of this one problem, as other confounding variables exist (e.g., variation between the two random independent samples). Nonetheless, these results are encouraging and provide a basis for future research.

Comparison of Final Exam Scores to Unfamiliar Question Scores

We found a medium correlation between unfamiliar question scores and final exam scores. The coefficient of determination F



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Figure 8. Comparison of final exam scores to scores on the unfamiliar question (Q3): (A) old format (2011, N = 200); (B) new format, year 2 (2013, N = 200).

Table 2. Distribution of Successful and Unsuccessful Answers on Each Interview Question Characterizations of Students’ Responses by Question Participants (N = 11)

Final Grade in OC2, %

1

2

3

4

5 6 2 10 9 4 3 11 7 1 8

88 80 87 90 97 84 89 65 76 60 85

Successful Successful Successful Successful Successful Successful Successful Successful Successful Unsuccessful Successful

Successful Successfula Successfula Successfula Incomplete Successfula Successfula Incompletee Incomplete Plausiblee Unsuccessful

Successful Successful Successfulb Successfulc Successfuld Successfulb Successfulc Successfuld Unsuccessfulc Unsuccessful Unsuccessful

Successful Successful Successful Successful Successful Unsuccessful Unsuccessful Unsuccessful Successful Successfule Unsuccessful

a

Incorrect configuration of a stereocenter. bPrompt 2 was given. cPrompts 1 and 2 were given. dPrompt 1 was given. eThe participant proposed an alternate reaction pathway.

(R2) values were higher for comparison of final exam scores to unfamiliar question scores than they were for comparison of final exam scores to familiar question scores. The larger R2 values suggest that, comparatively, the positive relationship between question scores and final exam scores (i.e., higher question scores resulted in higher final exam scores) was stronger for the unfamiliar question than for the familiar question. We also observed nearly the opposite relationship when comparing unfamiliar question scores and final exam grades in the sense that low scores on the question did not appear to predict low final exam scores but high scores on this question appeared to predict high final exam scores (Figure 8). In both years, students who received 0 out of 5 on the unfamiliar problem achieved final exam grades that were fairly evenly distributed anywhere between 18% and 90%. This indicated that achieving a low question grade did not predict an approximate final exam grade. At the other end of the spectrum, most of the students who scored 5 out of 5 on the question scored above 60% on the final exam; 13 and 14 students scored 5 out of 5 and above 90% on the final exam in the old and new formats, respectively. This relationship was not overly meaningful, as 60 to 100% is still a relatively wide range of scores. In both years, all of the students who scored 90% or higher on the exam achieved 5 out of 5 on the unfamiliar question, and no student with an exam grade below 90% achieved a score of 5 out of 5 on this question.

We analyzed students’ strategy use on exams and found that few students demonstrated the use of strategies such as mapping and expanding structures, even though such strategies have been correlated with success for synthesis problems31 and in the course students were taught and encouraged to use these strategies.39 Moreover, there were few differences in the rates of strategy use and errors between curriculum types or years. The full strategy use and common errors can be found in the SI. We analyzed interview data to more deeply explore the features to which students attended and the strategies they used when solving mechanism problems.

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FINDINGS: INTERVIEW ANALYSIS (RQ2 AND RQ3)

Success Rates

Table 2 summarizes participants’ success rates on each interview question and includes their overall course grades. Plausible solutions included proposals of products or mechanisms that differed from the expected ones but were nevertheless reasonable based on a student’s expected knowledge at a secondyear level. For example, one participant did not remember the full structure of LDA in question 4 and proposed NR2−, then showed the nitrogen atom in a nucleophilic role. We classified plausible solutions as successful, even if the participant did not provide the intended solution and regardless of what strategies they used. We had one exception to these definitions: if the student proposed a plausible mechanism to Q2 but gave the G



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just remember for the midterm and final and then everything just kinda like [explosion sound] out of my brain.” We recommend that the structures of reactants be drawn every time, only including the acronym or name if the structure is included as well. We strongly believe that a major purpose of learning organic chemistry is not to memorize acronyms but rather to learn to connect structure and reactivity. For these students, not knowing an acronym presented a barrier to analyzing the reactivity. Memorizing a compound’s role (e.g., LDA as a non-nucleophilic base) would pose a barrier to identifying situations when LDA or related species would be nucleophilic, such as a reaction with a small nucleophile (e.g., MeI). Few participants mentioned dipoles and none mentioned electron density, although most mentioned partial charges or electrophiles in conjunction with a reactive polar bond. No participants mentioned orbitals (e.g., hybridized, localized molecular orbital). More important than only mentioning features (implicit or explicit) was how those features were used in participants’ problem-solving processes. In the sections below, we describe how participants leveraged the properties of the implicit features they mentioned and which strategies they used in solving the problems presented.

incorrect configuration of the stereocenter, their answer was still classified as successful on this problem. We made this decision because multiple participants indicated that they were unsure about the configuration of the stereocenter because they did not have their molecular model kits to check it. Since molecular model kits are permitted during exams and are an encouraged strategy to predict the orientation of a molecule, we believe it would be inaccurate to describe these answers as unsuccessful. Answers were classified as incomplete when some of the mechanistic steps toward the product were drawn but the participant did not arrive at the final product. Features to Which Participants Attended (RQ2)

All participants identified the explicit features in the structures and during the mechanisms they drewsuch as explicit atoms (e.g., O, Mg), charges, and double bondsacross all reaction types, regardless of the participant’s ultimate success level in solving the problem. In other words, the explicit features served as cues that all participants used, but merely identifying cues did not make the difference in successfully proposing a plausible answer. Many implicit features were described by participants and used in their problem-solving; these features were both static and dynamic in nature. The features discussed by all participants included electrons (e.g., nonbonding electrons and electrons in bonds), bond breaking/forming, functional groups, reaction names, role of an acid/base, presence of a leaving group, and relative leaving group ability. Most participants mentioned partial charges, interpreted acronym names (e.g., LDA), electronegativity, electrophiles, and nucleophiles. One focus in the organic course even before the curriculum change was to identify, rank, and work with nucleophiles and electrophiles. This aspect of the course was made an even greater priority following the work of Anzovino and Bretz,15,16 who described students’ struggles with concepts of nucleophiles and electrophiles. In the present study, students’ regular use of these terms indicate that they are a regular part of their vocabulary. Moreover, the students used the terms purposefully and correctly each time (65 total instances across eight participants), although a more focused study would be needed to explore students’ concepts of nucleophiles and electrophiles. Every participant struggled with LDA (lithium diisopropylamide) in some way (Q4), wanting to draw its structure and identify its role but concerned that they would not remember and many did not (Figure 9). Only five participants correctly drew LDA (participants 2, 3, 5, 7, and 9), and two tried but made errors (participants 6 and 10). The other four participants could not remember the structure but assigned a role to LDA nonetheless. Participant 1 remembered LDA as being a strong nucleophile and proceeded to draw a perfectly plausible mechanism had that been its role. Participant 4 remembered LDA as a “big bulky base” and continued correctly with the mechanism. Participant 8 did not remember the structure or role and wanted to identify its role in the mechanism but could not proceed with an attempt at solving the problem: “I don’t remember the mechanism or how to go about this.” Participant 11 remembered LDA as adding a hydrogen but commented that there were “a bunch of these [acronyms] that I just had to memorize the names.” In the exam analysis, the vast majority of students expanded LDA correctly (92% and 94% in 2011 and 2013, respectively) but had clearly relied on rote memorization for the exam. Participant 11 emphasized this sentiment (Figure 9): “I was like,

Strategies and Errors (RQ3)

Students Showed Skillfulness in Many Areas Except Conformational and Stereochemical Analysis. Participants demonstrated skillfulness in many areas (e.g., using the EPF, drawing line structures, and assigning charges), except in conformational and stereochemical analysis. Every single participant used the EPF as part of their problem-solving process for every single question. Furthermore, they did so skillfully, consistently using the EPF correctly and as part of their exploration and descriptions of bond-forming and bondbreaking processes. Participants were not told to use the EPF or even to use a mechanism; instead, they were asked to “solve the following” or “explain how the reaction came to be”. The only exceptions to instructions to students occurred on Q3 with Participant 7, who asked whether “arrows and everything” should be drawn and the interviewer said yes, and on the same question with Participant 9, who was asked to show the mechanism to get to the product. In the present study, students used the EPF in chemically plausible steps as part of their problem-solving strategy, suggesting that students found the EPF useful as a tool, an important issue that has been identified in previous research. In one study with undergraduate students, participants decorated with arrows, only adding arrows after drawing the product because they were asked to do so and not because they found it helpful to get to the answer.14 In research with graduate students, participants drew curved arrows when asked to do so, but their arrows did not represent chemically plausible steps.6 Using the EPF as a tool helps describe the steps in a mechanism; the next phase (in a course) would be to examine the reasons why a reaction might proceed (or not) as shown, i.e., causal mechanistic reasoning.21,40 Students had strong skills with drawing and using line structures, expanding line structures, assigning and interpreting formal charges, and using electronegativity to determine partial charges and dipoles. In the 44 answers representing more than 200 chemical structures, we observed only two instances where a curved arrow would result in a pentavalent atom. While students demonstrated their proficiency with these skills, they did not H



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Figure 9. Every participant struggled to remember the structure and role of LDA. Interviewer’s handwriting is in black; participant’s handwriting is in green.

configuration of stereocenters with wedges or hashes. Five participants did not mention stereochemistry at all. Successful Strategies Involved Identified Dipoles and Charges. Most participants expanded structures to reveal partial or full charges explicitly in the first step of Q1, such as Participants 2 and 5 (Figure 10). Of those who did not explicitly identify charges, two successful answers involved directly recognizing and identifying the nucleophile and electrophile for the reaction; the unsuccessful answer identified neither charges (full or partial) nor roles of various species.

always choose to use them (e.g., few participants chose to expand implicit hydrogen atoms in Q3). These findings are consistent with the exam analysis, in which

How Are Students Solving Familiar and Unfamiliar Organic Chemistry

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