Student conceptualizations and predictions of substitution and elimination reactions: what are they seeing on the page?

Abstract

The current study aims to contribute to the literature on how organic chemistry students weigh various factors when predicting products of substitution and elimination reactions. This study focuses specifically on these mechanism types, as they are often the first instances where students must consider the “how” and the “why” of how reactions occur. Previous literature highlights that such reasoning can be challenging. To better support our students, it is essential to understand how they conceptualize these mechanisms. Here, we present results from an investigation into how students compare bimolecular and unimolecular substitution and elimination reactions (S_N1, S_N2, E1, E2). Students completed tasks involving case comparisons and “predict-the-product” exercises. Through the analysis of nine semi-structured interviews using coordination class theory, we found that (1) students placed a greater emphasis on the importance of the starting substrate in the outcome of a reaction, and (2) focused less on the function of the nucleophile or base in each reaction. Using coordination class theory, we identified visual features and knowledge elements that students coordinated, allowing us to create “resource graphs” that represented students’ conceptualizations. These graphs helped visualize the trajectories of students’ predictions by illustrating how they balanced multiple factors. We discuss implications for supporting students in distinguishing among reaction mechanisms.

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Introduction

Learning organic chemistry can be difficult for students, often creating a significant barrier for undergraduates pursuing STEM careers. Many students may struggle, with some ultimately withdrawing from the course or switching majors entirely (Weston et al., 2019). Given that STEM careers, such as example medicine, rely on a foundational understanding of organic chemistry to make informed decisions, supporting our students in organic chemistry courses is essential.

In organic chemistry courses, students are expected to develop the ability to use molecular-level ideas to predict and explain the behavior of chemical and biological systems (Cooper et al., 2010; Raker et al., 2013). However, instead of using molecular-level ideas, students often rely on rote memorization of chemical processes and their outcomes. To better understand how to support students, research has been conducted to understand students’ thought processes (Gao et al., 2024; Zaimi et al., 2024). Research findings suggest the importance of fostering reasoning beyond simple categorization (Caspari et al., 2018a, 2018b; Bodé et al., 2019; Crandell et al., 2020; Braun and Graulich, 2024).

Students need to consider multiple factors when determining how an organic reaction proceeds, yet multivariate reasoning has been recognized as a general challenge for learners, not just in organic chemistry (Bhattacharyya, 2006; Kuhn et al., 2008; Kraft et al., 2010; Weinrich and Talanquer, 2016; Talanquer, 2018). Although students may identify relevant variable, they often base their reasoning on a single variable when drawing conclusions (Kuhn and Dean Jr., 2004). Therefore, it is important to investigate how students’ conceptions of reactions and reaction mechanisms interact and inform one another.

Our work aims to understand how students coordinate multiple variables in their reasoning processes, providing a rich, descriptive insight into their thought patterns. Specifically, we investigate how students conceptualize S_N1, S_N2, E1, and E2 mechanisms. The focus is on the bimolecular and unimolecular substitution and elimination mechanisms since these are commonly the first reaction mechanisms. The focus on bimolecular and unimolecular substitution and elimination reactions stems from their role as foundational mechanisms introduced early in organic chemistry courses. These reactions are building blocks for more complex mechanisms and present challenges for students, as they require reasoning about a network of molecular-level variables to accurately predict reaction outcomes. Our central research question is: How do students use their conceptual understanding of substitution and elimination reactions to explain and make predictions?

Review of relevant literature

Student reasoning of organic reactions

The previous literature has shown that students often fixate on a single, explicit variable when making decisions in organic chemistry (Bhattacharyya, 2006; Graulich and Bhattacharyya, 2017; Finkenstaedt-Quinn et al., 2020). For instance, Bhattacharyya (2006) found that while students could correctly identify several factors influencing acidity—such as resonance, inductive effects, and steric effects—they tended to rely on one consistent factor: bond polarizability. Similarly, Anzovino and Bretz (2015) observed that students overly emphasized the presence of charges on chemical species. For example, students identified a species as a strong nucleophile solely based on the presence of a lone pair, without considering other important factors like electronegativity or polarizability. Given these tendencies, further research is needed to support students in coordinating multiple variables when reasoning about complex processes. This work should focus on gathering a richer description of how and why students prioritize certain criteria over others (Watts et al., 2021).

Additional studies have examined students’ understanding of key components of organic reactions, including nucleophiles, electrophiles, and leaving groups (Anzovino and Bretz, 2015, 2016; Popova and Bretz, 2018c). Other literature has highlighted students’ difficulty in classifying nucleophiles and bases (Cartrette and Mayo, 2011; Graulich et al., 2019; Frost et al., 2023). For instance, Cartrette and Mayo (2011) found that while students could define the terms nucleophile and electrophile, they had difficulty applying these definitions while solving mechanistic problems. Similarly, Cruz-Ramírez de Arellano and Towns (2014) found that students had gaps in identifying nucleophiles and bases, and often relied heavily on the starting material to determine the mechanism. For example, students assumed that a substrate with a double bond or a protic solvent would dictate a specific reaction mechanism.

In a study by Popova and Bretz (2018a), when students were asked to map substitution reactions onto reaction coordinate diagrams, they tended to focus only on the reactant, intermediate, and product. Students also described certain leaving groups as “good” but often struggled to explain what made a leaving group effective (Popova and Bretz, 2018c). This indicated that students may need additional support to move beyond focusing on surface features, such as charge, to a deeper understanding of reaction mechanisms (Popova and Bretz, 2018b).

Students’ mechanistic thinking

A common area of difficulty identified in the literature is students’ ability to reason about molecular-level explanations for reaction mechanisms. As a result, one of the central focuses in organic chemistry is mechanistic thinking. A significant body of recent research has explored students’ use of mechanistic arrows or electron-pushing (Bhattacharyya and Bodner, 2005; Ferguson and Bodner, 2008; Grove et al., 2012a, 2012b; Bhattacharyya, 2013, 2014; Galloway et al., 2017; Popova and Bretz, 2018a; Caspari and Graulich, 2019; Crandell et al., 2020; Houchlei et al., 2021).

While mechanistic arrows are not the focus of our research, it is important to recognize their use and the challenges they present to students. Students often struggle to use mechanistic arrows correctly, or neglect to use them altogether. For instance, Grove et al. (2012a, 2012b) observed that students frequently determined a product and then “decorate with arrows”, rather than using arrows as a predictive tool. Similarly, Caspari and Graulich (2019) found that students often predicted a product without initially drawing mechanistic arrows, later working backward from the product to place their arrows. Studies have shown that students who consistently use mechanistic arrows as a predictive tool are more likely to produce plausible products (Webber and Flynn, 2018; Houchlei et al., 2021). Furthermore, Crandell and colleagues (2020) noted that students’ accuracy improved as they became more effective in using mechanistic arrows in their responses.

As such, there are a number of reported curriculum redesign efforts aimed at supporting students’ mechanistic thinking (Flynn and Ogilvie, 2015; Cooper et al., 2019). Alternatively, some have suggested using representations such as electrostatic potential maps (Farheen et al., 2024; Nelsen et al., 2024) or data to explain how the reaction would proceed (Zhou and Moon, 2023) as scaffolds.

Weighing multiple factors

When explicit features of a reaction do not clearly indicate a single mechanism, students must weigh the importance of other reaction characteristics (Pabuccu and Erduran, 2017; Graulich et al., 2019; Demirdöğen et al., 2023). Bodé and colleagues (2019) found that when students were asked clarifying questions to explain their predictions for reaction outcomes, they considered factors such as stability, sterics, and energy. However, this focus on energetics was not consistent in all the studies, suggesting that students need additional support in integrating energetics into their reasoning (Eckhard et al., 2022; Pölloth et al., 2023). Overall, the extant literature has highlighted (1) the importance that students place on the explicit surface features of chemical reactions, and (2) the need to support students in connecting these features to underlying chemical functions by effectively weighing multiple factors.

Theoretical perspective

A framework is needed to model students’ understanding of substitution and elimination reactions that enables analysis of this broad concept while still detailing fine-grain knowledge structures. Coordination class theory (diSessa and Sherin, 1998) is well-suited to address this need. It provides a way to frame the large-scale concept of a topic while also capturing fine-grained details, allowing for a nuanced interpretation of students’ reasoning in a moment-to-moment context. The utility of coordination class theory as a tool is demonstrated in the wide range of applications in various science domains – physics (diSessa and Sherin, 1998; Wittmann, 2002; Thaden-Koch et al., 2006; Parnafes, 2007; Ozdemir, 2013; Buteler and Coleoni, 2016), computer science (Lewis, 2012), and more recently chemistry (Balabanoff et al., 2020; Rodriguez et al., 2020b, 2020c; Braun and Graulich, 2024).

What is a concept? An overview of coordination class theory

Originally developed in a physics context, coordination class theory operationalizes the broad idea of a concept and the ways that learners come to “see” that concept in the world (diSessa and Sherin, 1998). This framework builds on the knowledge-in-pieces perspective, which suggests that cognitive structures are not formed from large, stable ideas but rather from numerous fine-grained, dynamic, and highly context-dependent pieces of knowledge (diSessa, 1993; diSessa and Sherin, 1998).

Coordination class theory posits that the primary function of a concept, or “coordination class,” is to identify and interpret relevant information related to the concept across a wide variety of situations (diSessa, 2018). In other words, an individual's coordination class enables them to recognize and apply a concept across diverse scenarios. The coordination process relies on two key components: extractions and the inferential net. Extractions are the specific visual features that an individual focuses on in relation to a concept, while the inferential net consists of the knowledge elements used to draw conclusions about that concept (Parnafes, 2007; diSessa et al., 2016).

In a chemistry context, Rodriguez et al. (2020a) explored how students reasoned about reaction coordinate diagrams. For example, one extraction in their study occurred when a student focused on the peak of a reaction coordinate diagram while comparing two similar diagrams to predict which reaction would be faster. The student's focus on the peak served as a visual cue, activating the knowledge element that a higher peak correlates with a slower reaction (part of the inferential net), thus enabling the student to predict reaction speed. In an organic chemistry content, Braun and Graulich (2024) examined students’ reasoning in case-comparison tasks where resonance played a central role. They showed that extractions can be local—such as when a student focuses on a specific atom in a compound—or global, as when a functional group is considered for its influence on a compound's properties. The authors also described various extraction strategies used by students, such as focusing on electronic factors like charge while connecting knowledge elements (e.g., the idea that “like repels like”). These fine-grained details of extractions and knowledge elements within the inferential net illustrate how individuals coordinate information when drawing conclusions about a concept.

Rationale and research questions

A synthesis of the literature highlights two key points. First, it is essential to investigate how students reason about the mechanisms behind substitution and elimination reactions, as previous studies indicate that students need support in weighing multiple factors to accurately predict reaction outcomes. Second, substitution and elimination reactions are complex concepts, and understanding how students “see” these reactions is not straightforward. Using coordination class theory provides a valuable lens for this investigation. The contribution of this study lies in applying coordination class theory to examine how students make predictions about these reactions. Since this theory has yielded valuable insights in studies of other complex concepts, the findings of this study will contribute to the literature on how to effectively study students’ understanding of complex scientific ideas.

This study is guided by the following research questions:

1. How do students conceptualize substitution and elimination reactions?

2. What knowledge do students coordinate when predicting and explaining substitution and elimination reactions?

3. How do students identify and weigh different factors in predict-the-product exam tasks to inform their predictions?

4. How do students explain their reaction mechanism choice?

Methods

Research setting

Participants were students enrolled in Organic Chemistry 1 at a large Midwestern University in Spring 2022. “Organic Chemistry” by Wade and Simek was the textbook used in the course along with the instructors’ supplementary slides. The course was comprised of mainly science and engineering majors. Each week, students attended two 75-minute lecture sections and one 50-minute discussion section for review of course material. Data were collected within two weeks following the second exam, which addressed substitution and elimination reactions.

Ethical considerations

Students were informed that participation in the study had no impact on their course grade and that they could withdraw from the study at any time. All research activities were conducted in accordance with the university's Institutional Review Board. Students were compensated for their time with a $20 gift card upon completion of their interview.

Interview tasks

The primary data source for this study was semi-structured interviews, which included case comparison problems and “predict-the-product” tasks. The predict-the-product tasks mirrored the types of questions students encountered on exams. Fig. 1 provides examples of a case comparison and a predict-the-product task used in this study. The case comparison task presents an S_N1 and S_N2 mechanism, prompting students to identify features they associate with each mechanism. The predict-the-product task includes reaction conditions and asks students to predict the product and the mechanism by which the reaction occurs (i.e., S_N1, S_N2, E1, or E2). Follow-up questions were posed to clarify students’ thought processes based on their responses.

Fig. 1 Examples of the case comparison and predict-the-product tasks. The case comparison tasks present two contrasting cases that support students in attending to the similarities and differences, eliciting more of their knowledge. Predict-the-product tasks reflect the common open-ended problems asked on organic chemistry exams.

Recent literature in the chemistry community has highlighted the effectiveness of case comparison questions in eliciting more information than single-case questions (Alfieri et al., 2013; Roelle and Berthold, 2015; Graulich and Schween, 2018; Caspari and Graulich, 2019; Rodemer et al., 2020; Watts et al., 2021). Case comparisons contrast multiple cases rather than focusing on a single instance. For example, instead of presenting a single reaction, students might be shown two organic reactions with contrasting nucleophiles. Unlike traditional single-case problems, case comparison questions provide students with two reactions and prompt them to consider why one mechanism might be more favorable than the other. Studies have highlighted the effectiveness of case comparisons in semi-structured interviews, with some research also noting their use in instructional settings (Alfieri et al., 2013).

In addition to eliciting greater knowledge, DeCocq and Bhattacharyya (2019) found that students demonstrated stronger reasoning when the reaction product was provided, as in case comparison questions. Case comparison tasks offer a context for students to identify explicit features that are similar or different across problems, facilitating the expression of their deeper, implicit knowledge. They also help students attend to specific differences between tasks, supporting a more nuanced understanding (Caspari and Graulich, 2019). This aligns with coordination class theory, as it encourages students to focus on critical “extractions” or key elements in each task.

The other type of task used in the interviews, predict-the-product tasks, was designed to reflect a common problem type in organic chemistry exams. Students were asked to predict the product and identify the reaction type for a given set of starting materials and conditions.

The prompts were initially piloted with a group of students from a previous Organic Chemistry I course (Fall 2021). Additionally, we consulted three organic chemistry faculty members to gather evidence of content validity and further feedback on the questions. The final interview guide (provided in Appendix 1) consisted of three predict-the-product tasks and four case comparison tasks. All tasks used in the interview addressed the four reaction mechanisms: S_N1, S_N2, E1, and E2.

Data collection

The nine semi-structured interviews were audio and video recorded, and students used a Livescribe pen to answer questions. The Livescribe pen captured students’ writing in addition to audio, allowing us to view their drawings synchronized with their verbal responses (Linenberger and Bretz, 2012). During the interview, students first completed the three predict-the-product tasks, followed by the four case comparison tasks. This sequence was chosen to prevent the case comparison tasks from influencing responses to the predict-the-product questions, providing a more authentic context for those initial responses.

All audio data was transcribed verbatim and cleaned by the researcher, with language clarified and verbal pauses removed to improve readability. Participants were asked their preferred pronouns, and pseudonyms were assigned accordingly to respect their identities.

Data analysis

As discussed in the theoretical framework section, a coordination class consists of two main components: knowledge elements and the extractions. Our first stage of analysis focused on identifying these components in the interview data. We first developed working definitions for knowledge elements and extractions, specifically tailored for use in our coding process. These definitions, presented in Table 1, are informed by prior literature, particularly the work of Buteler and Coleoni (2016) in which knowledge elements were centered around inferential “if-then” statements. For our analysis, we adopted a more expansive definition for knowledge elements to allow for a broad range of reasoning types (definitions, inferences, etc.) that varied in structure and content. This approach allowed us to capture the diverse knowledge elements students used to draw conclusions.

Table 1 Coding scheme for knowledge elements and extractions. Based on the definitions developed by Buteler and Coleoni (2016)

Code	Definition
Knowledge element	Inferences/definitions/associations/heuristics/used to make a conclusion that helps address a specific problem or question. Knowledge used to solve the problem that is external to the prompt or problem and is a step that helps them progress through the prompt or problem.
Extraction	Visual feature inherent to a prompt/situation or a structure drawn by the student that is attended to by the student. Can be part of a knowledge element.

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For extractions, we limited coding to distinct visual features of the prompt that did not require substantial interpretation by the student. If a student identified a substitution as primary, secondary, or tertiary—without interpretation—this was coded as an extraction, given its lack of interpretive complexity. In contrast, if a student noted a “crowded leaving group,” this was coded as a knowledge element rather than an extraction due to the level of interpretation implied.

To aid in the consistent application of these definitions as a coding scheme, we have included detailed coding notes and examples from our data set in Appendix 2. These examples help illustrate how the codes were applied and provide further context for our definitions.

Our coding process for the full dataset began with the application of codes to a subset of data (two interviews) by two independent raters, followed by consensus-building discussions. In prior application of the coding scheme to pilot data, we consistently achieved kappa values reflecting moderate levels of agreement. For the full dataset, we used collaborative refinement of code application, reaching 100% agreement on these initial interviews and adjusting code definitions for clarity and specificity as necessary. After this initial phase, the primary researcher independently coded the remaining interviews, consulting the second coder only when uncertainties arose. This approach was inspired by Campbell et al. (2013), who discussed alternative coding methods to ensure a reliable analytic process.

Construction of resource graphs

Each transcript was coded for both knowledge elements and extractions, generating a wide range of data related to various aspects of organic reactions. These ranged from specific features about substitution or elimination reactions to broader concepts related to nucleophiles or bases. To make meaningful interpretations of how students conceptualized these reactions, we parsed these features into a more manageable dataset. Specifically, we filtered each student's responses to identify only the knowledge elements and extractions that explicitly related to one of the four reaction mechanisms (S_N1, S_N2, E1, and E2). This process segmented the data into focused, analyzable chunks that retained a rich amount of information on how students coordinated their thinking about the reactions. Additionally, we organized the tasks into three distinct areas from which students could draw information: the substrate, reaction conditions, and the product.

We used resource graphs (Wittmann, 2006) to represent the relationships between extractions and knowledge elements. Resource graphs provide a valuable way to visualize how these components interact as students reason through specific concepts (Rodriguez et al., 2020a, 2020d). An example resource graph, drawn from participant Ben, is shown in Fig. 2. For these graphs, we included only the knowledge elements and extractions from the case comparison tasks, as these tasks were specifically designed to examine how students conceptualize reactions.

Fig. 2 To discuss the knowledge elements and extractions in an organized way, the case comparison problems are divided into three sections: the substrate, reaction conditions, and the product. Example resource graph of Ben. This summarizes all the extractions (dotted line circles) and knowledge elements (solid line circles) that Ben associated with each of the four reaction mechanisms. Color indicates the region of the prompt to which each feature relates: substrate, reaction conditions, or product.

The resource graph summarizes the knowledge elements and extractions that Ben explicitly related to each of the four mechanisms across all four of the case comparison tasks (see Appendix 1). To further illustrate the construction of this resource graph, consider the quote from Ben as he discussed the S_N1 and S_N2 mechanisms in a case comparison problem. The bolded portions correspond to coded knowledge elements.

Ben: not a strong base, water, pK_aof 16. Not very strong. So that would favor S_N1, like, you want astronger base for S_N2. Yeah, cause it's like forcing the leaving group to eventually leave. Yeah, yeah. And it's bigger than OH, I mean, OH is areally small base that favors S_N2

Each of the bold sections from Ben represented a knowledge element that was relevant to a mechanism and was therefore included on his resource graph. Specifically, Fig. 2 shows “small base” and “strong base” for S_N2 and “not strong base” for S_N1.

To construct a complete resource graph, we applied this process for each student across all four case comparison problems. Features associated with more than one reaction are connected by a dotted line. Extractions are represented by dotted circles, while knowledge elements are shown in solid circles. The color of each circle corresponds to the specific region of the prompt to which the feature relates. It's important to note that we did not include the specific reaction mechanism as a source of information, as our focus was on the knowledge students used to infer the reaction mechanism. For the predict-the-product tasks, we used the same approach of identifying knowledge elements and extractions, mapping them directly onto the reaction prompt to retain the context of each reaction. In this work, we focus only on knowledge elements and extractions linked to the explicit sections of the reaction prompt (substrate, reaction conditions, and product), in alignment with coordination class theory's emphasis on explicit features. All student resource graphs are provided in Appendix 3 in the ESI.†

Results and discussion

An overview of student reasoning in the case comparison tasks

In Fig. 3, we summarize features attended to (extractions) and knowledge elements activated across all interviews in the context of case comparison tasks. These knowledge elements and extractions are grouped by mechanism type and are organized according to whether they corresponded to substrate, reaction conditions, or product. The grey dots outside of each knowledge element/extraction indicate the total number of students out of nine that associate that feature with a reaction.

Fig. 3 Summary of the knowledge elements and extractions that students associate with each of the four reaction mechanisms, organized by whether they pertain to the substrate, reaction conditions, or product. Extractions are indicated by the dotted circles. Knowledge elements are represented by solid lines. The grey dots outside of each knowledge element/extraction indicate the number of students that indicated that specific feature out of the total nine students.

The substrate. The feature that most students attended to across all mechanisms was the level of substitution of the leaving group on the substrate (1°, 2°, or 3°). Beyond this, only a few students discussed additional characteristics of the leaving group, such as its strength. When prompted, students could identify leaving groups as strong or weak but often needed more support to explain the reasoning behind their judgments. This aligns with findings by Popova and Bretz (2018c), who observed that while students could identify good nucleophiles, they often struggled to explain why a particular species was effective.

Our data suggest that, due to the frequent presence of halides in reactions, students may have been trained to recognize them as effective leaving groups without understanding the underlying properties. This familiarity enabled students to reason correctly about the reaction without a deep understanding of leaving group function. For elimination reactions, fewer students emphasized the substitution level of the leaving group; instead, their focus shifted toward the nucleophile and base.

Reaction conditions. Students attended to various aspects of the reaction conditions, focusing heavily on the nucleophile or base involved in each reaction. Specifically, they frequently considered the strength of the base or nucleophile, though this focus was not as dominant as their attention to the substitution level of the leaving group. When evaluating base or nucleophile strength, students often relied on pK_a values, using a cutoff of five as instructed.

A heuristic approach also emerged, where some students associated S_N2 and E2 reactions with metal cations, such as sodium or potassium. Rather than examining the base or nucleophile itself, these students focused only on the metal cation, using it as an indicator of an S_N2 or E2 reaction. Another common strategy involved students drawing formal charges around the base or nucleophile in each reaction. While this was productive for species like sodium hydroxide (i.e., Na⁺OH⁻), students tended to overapply this approach, dissociating all species—including weak acids like methanol (i.e., H⁺OCH₃⁻)—which led to incorrect assumptions about the nucleophilic species.

This overapplication appears to stem from students’ efforts to simplify the prediction of reaction products. For example, with methanol, although methoxide is not the nucleophilic species, students assumed it would be because it ultimately substitutes onto the compound. This “shortcut” led students to focus only on changes in the final product, often misidentifying nucleophiles in the process. This tendency presents challenges for students’ understanding of electron-pushing mechanisms, indicating a need for more support in accurately identifying nucleophiles like methanol rather than oversimplifying them as methoxide ions.

These findings underscore the importance of simpler reaction mechanisms as foundational for developing productive reasoning about chemical species and reaction mechanisms, including concepts like species dissociation in a reaction mixture.

The product. Although less detailed than other areas, students consistently focused on the presence or absence of double bonds when reasoning about each reaction mechanism. This feature was the primary indicator students used to confirm which mechanism was occurring. For example, several students inferred that an S_N2 reaction had taken place in an unfamiliar reaction simply because there was no double bond present in the product.

An overview of student reasoning in the predict-the-product tasks

In Fig. 4, we summarize the extractions and knowledge elements activated across all interviews in the context of the predict-the-product tasks. Like Fig. 3, knowledge elements and extractions are grouped by mechanism type and organized according to whether they relate to the substrate or reaction conditions. Since students are tasked with predicting the product in these exercises, the product is not initially present and therefore is not shown in the figure. Dark gray dots indicate the number of individual students who used each knowledge element or extraction or selected that mechanism. Additionally, the reaction type chosen by students is displayed on the right side of each figure. Throughout this section, each task will be referenced by the nucleophile used in each reaction to distinguish them (i.e., NaOH task, NaSCH₃ task, and HOCH₃ task).

Fig. 4 Summary of knowledge elements and extractions students associate with the NaOH, NaSCH₃, and HOCH₃ predict-the-product tasks. A knowledge element is indicated by a solid line circle and an extraction by a dotted line circle. Filled dark grey circles indicate the number of students that used that knowledge element or extraction and chose a specific mechanism.

The substrate. Fig. 3 reveals that, across all prompts, students commonly identified the leaving group on the substrate and correctly classified its substitution level as primary, secondary, or tertiary. When discussing the leaving group, most students simply noted the halogen's role as a leaving group, with only a few discussing strengths. In nearly all cases, this was the only information students associated with the substrate. A notable exception was in the HOCH₃ task, where one student also focused on the dashed line representing the leaving group and inferred that this indicated a change in stereochemistry during the reaction. Overall, this analysis shows that most students consistently attended to the nature of the substrate in each reaction, particularly the substitution level of the leaving group—mirroring the preference observed in the case comparison tasks.

The reaction conditions. Among the various factors influencing a reaction mechanism (substrate, nucleophile, leaving group, solvent, and temperature), the most frequently attended feature by students was the nucleophile. Many students predicted that the nucleophile would dissociate into ions, a noteworthy observation given that dissociation in some species, such as methanol, reflects scientifically non-normative ideas. Another common trend was students’ classification of the nucleophile as strong or weak based on prior knowledge. Beyond these patterns, students’ knowledge elements related to reaction conditions were idiosyncratic, with no consistent themes emerging across the sample.

In the predict-the-product tasks, students generally succeeded in analyzing the substrate and making accurate associations about the mechanism based on substrate classification (primary, secondary, tertiary). This preference for focusing on the substrate in predict-the-product tasks aligns with students’ reasoning in the case comparison tasks, where the substrate was also the most associated feature for each reaction. Students’ similar approach across both types of tasks suggests a consistent conceptualization of reactions, particularly in their emphasis on different reaction species.

How do students explain their reaction mechanism choice?. Next, we will examine the features students used to explain the reaction outcomes. This analysis will help us understand how students weighed multiple factors—such as substrate, nucleophile, leaving group, solvent, and temperature—when assessing the starting materials and conditions of a reaction. Fig. 5 provides a summary of the products and reaction mechanisms selected by students across the three predict-the-product tasks. The number of students choosing each product or knowledge element/extraction is indicated by filled dark gray circles next to each feature. Additionally, the responses of Ben and Jan are highlighted with a yellow outline, as they will be discussed in a subsequent section.

Fig. 5 Summary of the products drawn, and knowledge elements/extractions used by students to justify their choice of mechanism for the predict-the-product tasks. Filled dark grey circles indicate the specific number of students that drew that product or used that evidence to support their choice out of the total number that chose the mechanism (Ben and Jan's answers are outlined in red). The colored circles in the reasoning indicate which feature of the reaction it is connected to: maroon for substrate, gold for reaction conditions, blue for product, and grey for mechanistic process.

Fig. 5 presents the products drawn by students for each of the reactions (NaOH, NaSCH₃, and HOCH₃) and indicates the number of students who focused on various factors (substrate, nucleophile/base, leaving group) for each predicted product. The figure provides evidence that, even when students predicted the same product, the factors they considered and the weight they placed on each factor in their explanations varied significantly.

A common trend across both predict-the-product tasks was that students often based their explanations of the reaction mechanism on features of the substrate. For instance, in the most frequent mechanism choices for the NaOH prompt (S_N2 and S_N2/E2), five of the nine students cited the primary substrate as their justification. Other features, such as the nucleophile or leaving group, were considered by only one or two students. Another noteworthy observation is the prevalence of logically non-normative reasoning, particularly the use of the predicted product itself to retroactively justify their choice of mechanism.

The gray circles indicate instances where a student used the mechanism itself to explain their final choice of reaction type, creating a circular argument. In these cases, students justified their choice of reaction type by referencing the steps within the mechanism rather than external evidence. Overall, these data suggest that students lack consistent approaches for explaining their mechanistic choices, highlighting the need for support in using the substrate and reaction conditions as evidence to explain reaction outcomes. Furthermore, as observed throughout the dataset, students’ focus remained largely on the substrate, which was also evident in how they explained their mechanistic choices.

Connecting across the tasks: insights from case comparison and predict-the-product tasks

Overall, the data from the case comparison tasks data suggest that students possess rich underlying knowledge about these reactions, from understanding the substitution level of the leaving group to recognizing the number of steps in a mechanism.

In the case comparison tasks, students were provided with both the product and the mechanism; however, on exams, they are required to use only the substrate and reaction conditions to predict and explain the mechanism. To enhance students’ accuracy in predict-the-product tasks, two areas of focus are recommended: first, helping students attend more closely to the features of the substrate and nucleophile, and second, supporting them in connecting these features to the functional roles of the substrate and nucleophile in a reaction. These findings align with suggestions in previous literature (Cruz-Ramírez de Arellano and Towns, 2014; Anzovino and Bretz, 2015; Popova and Bretz, 2018c).

In the predict-the-product tasks, students frequently predicted that the nucleophile would dissociate into constituent ions. While this approach was sometimes useful for identifying charged nucleophiles, students tended to over-apply this approach, even to weak nucleophiles that would not typically dissociate strongly in solution. This strategy likely developed to help students identify the active component of the nucleophile in the final product while bypassing neutralization steps.

Beyond dissociation, the most noted feature of the nucleophile among students was its strength or weakness. Fig. 5 shows that a few students used nucleophile strength and size to justify their chosen reaction mechanisms; however, this reasoning was primarily attributed to just two students—Ben and Jan, whose responses are highlighted by the yellow-outlined gray circles in Fig. 5. Outside of Ben and Jan, few students considered the nucleophile's function or characteristics when selecting a mechanism. Ben and Jan accounted for six out of the eight total references to nucleophile strength across all predict-the-product tasks. This suggests that most students overlooked one of the nucleophile's critical features—its strength, which significantly influences its function in a reaction. Instead, they relied on heuristic, shortcut associations, such as assuming dissociation into ions or using other idiosyncratic cues, as illustrated in Fig. 5.

Additionally, the predict-the-product tasks elicited a more limited subset of students’ knowledge compared to the case comparison tasks. For example, in the case comparison tasks, six students identified nucleophile strength in one or more reactions. However, in the predict-the-product tasks, only four students specifically considered nucleophile strength, with only Ben and Jan consistently attending to it across all prompts. In the following quotes, Pam addressed nucleophile strength in only one of the prompts.

Pam during the S _N 1/S _N 2 case comparison task : I kind of mentioned earlier for this S _N 1, it's water. And water, it's a very weak nucleophile or base. So S _N 2 has to have a strong nucleophile or base. So looking at water just tells you it has to be S _N 1 or E1 because of that.

Pam during the NaOH predict-the-product task : I chose S _N 2 because this is a first degree, like primary carbocation, or like carbon. And the chlorine is a good leaving group… If it's on a first degree, it has to be S _N 2.

Pam's responses in the case comparison task demonstrated her understanding of the importance of a strong nucleophile for the S_N2 mechanism. However, in the predict-the-product task, her reasoning focused solely on the substrate.

Her reasoning aligns trend described in the literature which suggest that students often rely on a single variable to solve problems, even when multiple factors are relevant. In this case, the single variable was the substrate, while other important variables—such as nucleophile, solvent, and others—were overlooked (Bhattacharyya, 2006; Kraft et al., 2010; Caspari and Graulich, 2019). Students tend to adopt heuristics such as this as a route towards the correct answer, which minimizes cognitive effort (Tversky and Kahneman, 1974; Graulich, 2015). While these heuristics can often lead to correct answers with less effort, they can restrict students’ development of multi-variate reasoning, which is essential for understanding the diverse factors that influence reaction outcomes.

As seen in the case comparison tasks, students could identify many of the key features in reaction mechanisms. Yet, when asked to explain and predict those same reactions in the different context of predict-the-product tasks, students had difficulty coordinating multiple features. Relatedly, Pam's dialogue suggested that the problems we present to students on exams may not be accurately assessing their true knowledge about reactions. Instead, they may be only activating a subset of their knowledge about reactions. These findings showed that students do have productive knowledge about substitution and elimination reactions but need support in activating that knowledge in a productive way on exams.

Moving past features towards function. In summary, when predicting reaction type, our data suggest that students often place heavy emphasis on the substrate of the reaction, prioritizing it over considerations of nucleophile strength or size. When thinking about the substrate, students tended to focus on identifying the leaving group and its substitution level. This focus on the leaving group aligns with previous research, which has shown that students are generally successful at identifying effective leaving groups, even if they lack an understanding of the underlying factors that determine leaving group strength (Popova and Bretz, 2018c).

In contrast, nucleophile strength is more continuous and lacks explicitly discrete characteristics, which may explain why students gravitate toward heuristic approaches when thinking about the nucleophile. For instance, Phyllis categorized nucleophiles as either protic or aprotic (see Phyllis's resource graph in Appendix 3 in the ESI†). Additionally, students’ knowledge about leaving groups tended to rely more on substitution level than on strength, suggesting a preference for more discrete, readily identifiable features rather than more fluid, nuanced reasoning. This inclination may reflect a general tendency to favor clear-cut inferences over reasoning that requires distinguishing subtle differences.

We observed that students also struggled in connecting the structural features of a compound to its function in a reaction. For instance, students who identified the strength of a leaving group often attributed this simply to it being a halogen, without recognizing the deeper electronic properties that contribute to its stability as a leaving group. Similarly, students tended to judge nucleophile strength based on rote cues, such as the presence of a counterion like sodium, and struggled to assess the strength of less familiar nucleophiles. For example, they did not recognize that a thiolate ion's characteristics are analogous to those of a hydroxide ion.

As instructors, we recognize that the aspects we emphasize when teaching problem-solving strategies strongly shape what students focus on. This work highlights that students and instructors often “see” different things when analyzing a reaction. Supporting students in developing more expert-like thinking is crucial for fostering a deeper, more integrated understanding of organic reactions. It's essential to consistently emphasize the implicit features of compounds that drive their behavior, encouraging students to move beyond memorized shortcuts. While experts may intuitively understand why a halogen is an effective leaving group, students still need guidance to make these connections by linking observable features to underlying chemical principles.

Limitations

This study explored how nine students conceptualized substitution and elimination reactions, focusing on their abilities to predict and explain these processes. Developing fine-grained coding definitions for these data proved a challenging, making our codes potentially context-specific and not widely generalizable. Although we reached a 100% negotiated agreement, we were unable to establish a high degree of inter-rater reliability. Future research could focus on creating more universally applicable coding definitions to enhance consistency across studies using coordination class theory.

Our work was also limited by the simplicity and number of interview tasks, which did not include more complex concepts like rearrangements or stereochemical requirements (e.g., anti-periplanar alignment in E2 reactions). Students were not given experimental data, which could be a valuable addition in future studies, allowing students to draw on data to classify mechanisms more robustly.

Another limitation was that students may not have fully articulated all aspects of their reasoning, as the prompts focused on explanation without specific requirements for energetic considerations. Additionally, without classroom observations, we could not assess the instructional impact on students’ thinking, which we suggest as a future research direction. Moreover, as we did not collect grade data, we were unable to connect reasoning patterns to course performance.

Our small sample size limits generalizability, and convenience sampling may not represent the full spectrum of student performance. Despite these limitations, we observed notable variations in students’ reasoning and encourage future research to build on the initial themes identified in this exploratory study.

Conclusions and implications

Implications for practice: utilizing coordination class theory in instructional activities

This study revealed two key insights relevant to instruction in organic chemistry courses. First, students placed a strong emphasis on the substrate when conceptualizing and solving problems related to substitution and elimination reactions, often at the expense of other factors. Second, while students demonstrated productive knowledge about these reactions, they struggled to apply it effectively in the context of open-ended exam-style problems.

According to coordination class theory, students conceptualize reactions by focusing on specific features (extractions) and making connections to elements of prior knowledge (knowledge elements). To support students’ ability to reason about the impact of the nucleophile, instructional activities might explicitly guide students to focus on the nucleophile and to connect relevant inferences to it. An example of such an activity, based on a case comparison task from the interview prompt, is provided in Fig. 6. We encourage instructors to adapt these activities to fit their own courses. For instance, language in the example in Fig. 6 can be modified to ask students to provide reasoning for minor products by detailing the mechanism through which those products would form.

Fig. 6 Example problems of a scaffolded activity question that encourages students to attend to species in the reaction mixture and connect features to those species that inform the reaction's outcome. The full activity is presented in Appendix 4 in the ESI.†

A recent scoping literature review on organic chemistry education highlighted the need for instructional resources that support students in critically analyzing reaction features and recognizing relevant features across different contexts (Dood and Watts, 2022). This task directs students’ attention to all aspects of the reaction, prompting them to connect specific knowledge to each compound involved. This approach may help students weigh multiple pieces of evidence when determining mechanistic pathways, which has been a well-documented challenge for students in organic chemistry (Caspari and Graulich, 2019; Lieber and Graulich, 2020; Watts et al., 2021). Prompts like these, which encourage students to reason about multiple compounds within a reaction and understand how each compound's function contributes to the reaction's outcome, may be productive routes towards supporting students in critically analyzing reaction features and recognizing relevant features across different contexts.

Implications for research: insights into coordination class theory

The analysis in this work demonstrates the value of coordination class theory as a theoretical and analytical framework for understanding how students conceptualize ideas. This study contributes to the growing body of literature applying this framework in chemistry (Balabanoff et al., 2020; Rodriguez et al., 2020a, 2020d; Braun and Graulich, 2024). Coordination class theory recognizes the diverse knowledge that students draw upon to make conclusions about complex concepts. Rather than simply cataloging misconceptions, this approach highlights students’ productive knowledge and identifies areas where they may benefit from additional support and guidance.

We encourage researchers to continue using knowledge-in-pieces frameworks that more accurately model students’ thinking and learning processes (Hunter et al., 2022). Organic chemistry represents a challenging transition for students moving from general chemistry, as they shift from a product-oriented to a process-oriented understanding of reactions (Anderson and Bodner, 2008). Coordination class theory offers a promising approach for longitudinal studies that could reveal how students’ conceptualizations of substitution and elimination reactions evolve over time, providing valuable insights for instructional support and best practices.

Data availability

The data [student interviews] are not publicly available as approval for this study did not include permission for sharing data publicly. The data [resource graphs] supporting this article have been included as part of the ESI.†

Conflicts of interest

There are no conflicts to declare.

Appendices

Appendix 1 Full interview prompt

Thanks for taking the time to come in for this interview. Before we get started, I have just a couple of general questions for you.

What major and year are you here at the university?

What is your experience with chemistry?

For these interviews, we eventually will use a pseudonym as part of the anonymization of data. When we do this, which pronouns would you prefer we use to represent you?

Please provide a product and mechanism for the formation of product (S _N 1, S _N 2, E1, E2) for each of following reactions.

Both of the reactions above are correct. Can you explain why the different products form for each reaction?

Based on the substrate and reagent, what features about the reaction tell you which product will be the major product?

A student proposed that both of the following products are possible for the above reaction. Can you explain why this is correct?

Which set of products do you think will be the major products for the set of reactions above?

Revisiting this question, can you predict the product and the mechanism by which that product would form?

Revisiting this question, can you predict the product and the mechanism by which that product would form?

Revisiting this question, can you predict the product and the mechanism by which that product would form?

Appendix 2 Full code definitions with notes and examples. Highlighted portion in the examples represents the final coded portion

	Code definition	Coding notes	Examples
Knowledge element	Inferences/definitions/associations/heuristics used to make a conclusion that helps address a specific problem or question. Knowledge used to solve the problem that is external to the prompt or problem and is a step that helps them progress through a problem/prompt.	These relations are often indicated by linking words such as “so, because, if/then, since, which, I know (i.e. because of the heat, it's going to be E1 or E2). Knowledge elements are typically 1–2 sentences in length and often include an extraction as part of the phrase. Cannot be purely descriptive such as the description of features (this is a methyl, so it has 3 hydrogens).	“Because I know there's a double bond, they have to share a lone pair. So that means they both have to have lone pairs to give.”
			“Because I know that E1 and S N 1 usually occur at the same time, because you can't really regulate, which one happens. Both would probably occur”
			“This would be a good leaving group and that technically this is a good base. I don't think it's a good nucleophile. So, I'd say this is an S N 1 reaction, maybe”
			“OH, that's a strong nucleophile” (association)
Extraction	Visual features inherent to a prompt/situation or structure drawn by the student that are attended to by the student. Can be part of a knowledge element.	These usually come after indicator words such as “this, that, here, looking at, etc.” They commonly are part of a knowledge element in the inferential net. Can also be noted as explicitly not present in the prompt (I.e. “so there's no solvent here”).	This would be a good leaving group
		In order to be coded, we must be confident in what the student is referencing. It might include colloquial phrasing, such as “primary carbon”.	Looking at the I(odine)
			This is a good base
			That extra hydrogen
			Left with the OH
			This carbon, that the leaving group is attached to

Acknowledgements

This research represents a large portion of the work in the first author's graduate school career. And because of that, there are so many people whose insight and knowledge influenced this work. Dr Lira, Dr Williams, Dr Cole, Dr Forbes, Dr Scharlott, Dr Rodriguez, and Vinay Bapu Ramesh. The first author thanks them for all their support and time spent. The first author would also like to thank their network of friends and loved ones in Iowa who kept them going: Rachel, Sam, Lizzie, Eylül, Cely, Hoang, Trent, Shelly, and many others.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4rp00204k

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