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DOI: 10.1039/D0RP00329H (Paper) Chem. Educ. Res. Pract., 2021, 22, 733-748

Chemistry instructors’ intentions toward developing, teaching, and assessing student representational competence skills

Maia Popova * and Tamera Jones
University of North Carolina at Greensboro, Greensboro, North Carolina, USA. E-mail: m_popova@uncg.edu

Received 28th October 2020 , Accepted 16th April 2021

First published on 29th April 2021

Abstract

Representational competence is one's ability to use disciplinary representations for learning, communicating, and problem-solving. These skills are at the heart of engagement in scientific practices and were recognized by the ACS Examinations Institute as one of ten anchoring concepts. Despite the important role that representational competence plays in student success in chemistry and the considerable number of investigations into students’ ability to reason with representations, very few studies have examined chemistry instructors’ approaches toward developing student representational competence. This study interviewed thirteen chemistry instructors from eleven different universities across the US about their intentions to develop, teach, and assess student representational competence skills. We found that most instructors do not aim to help students develop any representational competence skills. At the same time, participants’ descriptions of their instructional and assessment practices revealed that, without realizing it, most are likely to teach and assess several representational competence skills in their courses. A closer examination of these skills revealed a focus on lower-level representational competence skills (e.g., the ability to interpret and generate representations) and a lack of a focus on higher-level meta-representational competence skills (e.g., the ability to describe affordances and limitations of representations). Finally, some instructors reported self-awareness about their lack of knowledge about effective teaching about representations and the majority expressed a desire for professional development opportunities to learn about differences in how experts and novices conceptualize representations, about evidence-based practices for teaching about representations, and about how to assess student mastery of representational competence skills. This study holds clear implications for informing chemistry instructors’ professional development initiatives. Such training needs to help instructors take cognizance of relevant theories of learning (e.g., constructivism, dual-coding theory, information processing model, Johnstone's triangle), and the key factors affecting students’ ability to reason with representations, as well as foster awareness of representational competence skills and how to support students in learning with representations.

Introduction and theoretical framework

One reason why chemistry can be challenging for learners is chemists routinely use a wide variety of visualizations to communicate about chemical phenomena (e.g., two-dimensional graphs and diagrams, concrete ball-and-stick models, symbolic representations such as chemical symbols, formulas, and equations). Besides the sheer number of representations used in chemistry, students also need to learn to make sense of phenomena across multiple distinct domains: submicroscopic (chemical particles that are invisible to the naked eye), macroscopic (tangible and visible phenomena), and symbolic (symbols and characters used to represent phenomena within the macroscopic and submicroscopic domains) (Johnstone, 2006, 2010; Taber, 2013). Consequently, when learning chemistry, novices face the complicated task of having to master the “language” of chemical representations. For example, proficiency in using symbolic representations alone can be compared to learning a foreign language, where chemical symbols from the periodic table resemble letters from the alphabet, chemical formulas resemble words, and reaction equations resemble sentences that convey meaning about invisible dynamic interactions between sub-microscopic particles. Learning chemistry, therefore, requires both mastery of complicated abstract concepts and the development of representational competence (RC). RC is defined as the “set of skills and practices that allow a person to reflectively use a variety of representations or visualizations, singly and together, to think about, communicate, and act on chemical phenomena in terms of underlying, aperceptual physical entities and processes” (Kozma and Russell, 2005, p. 131). In this definition the terms representation and (external) visualization are used as synonyms and are positioned as “perceptible, symbolic images and objects in the physical world that are useful to represent aspects of chemical phenomena, much of which cannot be seen” (Kozma and Russell, 2005, p. 123). Kozma and Russell synthesized findings from studies investigating differences in expert-novice investigative practices (Dunbar, 1995; Goodwin, 1995; Kozma and Russell, 1997) and proposed a set of concrete skills that describe how chemists respond to and use different representations of chemical phenomena. These skills include an ability to: (a) use representations to describe observable chemical phenomena in terms of underlying molecular entities and processes, (b) select or generate a representation and explain why it is appropriate for a particular purpose, (c) interpret features of a particular representation, (d) make connections across different related representations by mapping features of one representation onto those of another, (e) take the epistemological position that representations correspond to but are distinct from the phenomena that are observed, (f) describe limitations and affordances of different representations, and (g) use representations in social situations to support claims, draw inferences, and make predictions about chemical phenomena (Kozma and Russell, 2005). The latter two skills have been positioned as higher-level meta-representational skills (diSessa, 2004; Schönborn and Anderson, 2006).

Several assumptions underly RC skills. First, a developmental trajectory is assumed, which means that as learners become more integrated in the learning of chemistry, they progressively become more proficient in using its representational system. With prolonged exposure to learning about chemistry, representations become a useful tool for constructing and communicating understanding. RC, therefore, develops in sophistication over time (Kozma and Russell, 2005). Second, different levels of RC can be developed with different types of representations. That is, a learner may master most of the skills listed above with respect to a particular representation (such as Lewis structures) and only be able to interpret features of another (such as Newman projections) (Kozma and Russell, 2005). Finally, in chemistry, RC and conceptual understanding inform one another. That is, one cannot have a high level of RC and completely lack conceptual understanding and vice versa. In other words, mastery of chemical concepts is achieved in tandem with mastery of the visualizations used to represent and explain those concepts (Kozma and Russell, 2005). In fact, Schonborn and Anderson (2009; 2010) proposed an empirical model that suggests that learning with representations is affected by three interconnected factors: knowledge of concepts associated with a given representation (conceptual), cognitive skills that a student must use to make-sense of a representation (reasoning), and the nature of the given representation (mode). Successful sensemaking of representations requires simultaneous engagement with the three aforementioned factors.

Rationale and significance

A historical analysis of 18 organic chemistry American Chemical Society (ACS) exams showed that since 1982 more than 90% of exam items include representations (Raker and Holme, 2013). In addition, when mapping key undergraduate chemistry content, the ACS Examinations Institute included visualizations as one of ten anchoring concepts (Holme et al., 2015). The inclusion of visualizations as an anchoring concept and its extensive use in assessments is not surprising due to the chemists’ heavy reliance on visualizations to support thinking and communication about chemical phenomena. Chemists routinely use multiple representations to reason about molecular structure and chemical processes underlying molecular transformations. The ability to reason with representations is also an integral component of the scientific and engineering practices proposed by the National Academies of Science (The National Research Council, 2012). Examples of science and engineering practices that rely on RC skills include “developing and using models,” “planning and carrying out investigations,” “analyzing and interpreting data,” “using mathematics and computational thinking,” and “obtaining, evaluating, and communicating information.” The 3-Dimensional Learning Assessment Protocol (3D-LAP) (Laverty et al., 2016) provides concrete criteria to elicit evidence of student engagement with scientific practices. For example, an assessment question designed to elicit students’ ability to “develop and use models” should meet all four of the following criteria: (1) give an event, observation, or phenomenon for the student to explain or make a prediction about, (2) give a representation or ask the student to construct a representation, (3) ask the student to explain or make a prediction about the event, observation, or phenomenon, and (4) ask the student to provide the reasoning that links the representation to their explanation or prediction (Laverty et al., 2016; Stowe and Cooper, 2017). As shown above, criteria 2 and 4 heavily rely on RC skills such as the ability to (a) interpret features of a particular representation, (b) generate a representation, and (c) use representations in social situations to support claims, draw inferences, and make predictions about chemical phenomena (Kozma and Russell, 2005). As evidenced in this example, RC skills are necessary components and prerequisites for student engagement in scientific practices.

Despite the importance of RC for student learning, the development of this set of skills is not a trivial task. While many difficulties that students face when using representations are associated with their lack of prior knowledge or lack of an understanding of the affordances and limitations of representations, some students’ challenges are an artifact of the instruction that they receive (Bodner, 1991; Garnett et al., 1995; Nathan et al., 2001; Rushton et al., 2008). Due to their high level of expertise, instructors might have a limited level of awareness of the skills required to understand a concept or solve a problem – a phenomenon known as the expert blind spot (Nathan et al., 2001). For this reason, instructors might select representations that are challenging for students to understand, might overlook the importance of carefully scaffolding representations in supporting student learning, and/or might assume that students understand the affordances and limitations of various representations (Schönborn and Anderson, 2010; Baldwin and Orgill, 2019). Although a wide body of literature has focused on exploring chemistry students’ RC skills (Bodner and Domin, 2000; Cooper et al., 2010; Grove et al., 2012; Stull et al., 2012; Popova and Bretz, 2018b, 2018c), very few studies have examined chemistry instructors’ approaches toward developing student RC (The National Research Council, 2012). The most prominent study in this area to date is the characterization of 494 biochemistry instructors’ views toward developing and assessing visual literacy in their courses (Linenberger and Holme, 2015). Linenberger and Holme found that 57% of biochemistry instructors assume visual literacy on their assessments. Additionally, these instructors selected the ability to interpret and generate representations as the two most important visual literacy skills to be developed in a biochemistry course. Despite these important findings, the authors acknowledged that the study's limitations are associated with eliciting instructors’ responses via an online survey and it is unclear how well the participants understood each visual literacy skill when ranking their importance. Bussey and Orgill (2019) argued that instructors’ intentions around student learning with representations directly impact the curriculum enacted in the classroom. They interviewed 5 biochemistry instructors about their intentions for student learning to evaluate and select external representations of protein translations and found that instructors favored simple, static, stylized representations that closely aligned with their learning goals around teaching about protein translation. Nitz and colleagues (Nitz et al., 2014) explored the development of RC along with the development of conceptual understanding when learning about photosynthesis. They found that although the two constructs are interactively related, the biology students in their study had significant increase in their content knowledge, but not in their RC. The authors argued that this might be due to only moderate focus on supporting students in the development of their RC. Kohl and Finkelstein (2006) investigated the effect of instructional environment on physics students RC. They found that students in a reform-style introductory physics course that placed a higher emphasis on supporting students in mastering multiple representations learned a broader set of RC skills than students in a traditional course. Xue and Stains (2020) highlighted the importance of studying instructors and how their instructional decisions impact student learning outcomes. As part of a larger study, they compared the teaching practices of two organic chemistry professors and analyzed the relationship between the employed teaching and students’ understanding of resonance. They found a significant difference in student performance, with one section having a better conceptual understanding of resonance than the other. Analyzing instructional approaches revealed that the instructor of the section with higher performance focused their instruction on affordances and limitations of chemical representations, whereas the other instructor emphasized the features and processes of recognizing and drawing resonance structures. These findings suggest the importance of supporting students in the development of meta-representational competence skills.

This exploratory study aims to contribute to this limited body of literature by characterizing chemistry instructors’ intentions toward developing student RC skills. Specifically, to gain a comprehensive understanding from a diverse body of participants, this investigation focuses on studying chemistry instructors from 11 different universities across the US that are teaching a variety of chemistry courses. Along with a thorough analysis of instructors’ intentions toward developing student RC skills, we also provide insights into the resources and support that chemistry instructors desire with respect to supporting student RC. In particular, this study answers the following research questions:

1. What are chemistry instructors’ intentions toward developing, teaching, and assessing student representational competence skills?

2. How aligned are instructors’ goals toward developing student representational competence and their described instructional and assessment practices?

3. What resources and support do chemistry instructors identify as desirable for teaching with representations more effectively?

Methods

Sample

Thirteen assistant and associate chemistry professors participated in this Institutional Review Board approved study. All participants were from research intensive institutions, according to the Carnegie's classification (Center for Postsecondary Research), that are located across ten different states in the United States (US), spanning all four of the regional divisions distinguished by the US Census Bureau (U.S. Department of Commence): 5 universities in the South, 3 in the West, 2 in the Northeast, and 1 in the Midwest. Detailed demographics for the sample are shown in Table 1. To protect the identities of the participants, we created a code number for each instructor (P1–P13). All courses taught by the participants were undergraduate level courses, except for the polymer chemistry and bioanalytical chemistry courses taught by P3, P6, and P7.
Table 1 Participant demographic informationa obtained from a survey used to recruit faculty for this study
Course Years teaching Sex
a Information about race and ethnicity was not collected.
P1 General chemistry 5 F
P2 Inorganic chemistry 4 F
P3 Bioanalytical chemistry 3 M
P4 Analytical chemistry 4 F
P5 Analytical chemistry 4 F
P6 Polymer chemistry 7 M
P7 Polymer chemistry 7 M
P8 Biochemistry 3 M
P9 Biochemistry 5 F
P10 Organic chemistry 3 M
P11 Organic chemistry 5 M
P12 Organic chemistry 7 F
P13 Organic chemistry 12 M

Data collection

Instructors participated in semi-structured interviews (Drever, 1995) that were conducted in Spring 2019. The interviews lasted approximately thirty minutes. Because this population was geographically diverse, multimedia-based programs (e.g., Skype, Zoom) were used to conduct the interviews. An audio recorder was used to capture the interviews. All participants were provided with a consent form that described their rights and how the data would be treated to ensure confidentiality. The interviews started with a general introduction of the nature of the study and a description of the interview protocol. Despite the provided description, the participant who was interviewed first was unfamiliar with the term “representation,” initially interpreting it as tools that instructors use to communicate information in the classroom (e.g., PowerPoint presentation, white board). For this reason, prior to the beginning of the rest of the interviews, the interviewer also provided participants with a definition of the term “representation” and a few examples of representations commonly used to teach chemistry. The following definition and examples, were used: “Representations are perceptible, symbolic images and objects in the physical world that are useful to represent aspects of chemical phenomena, much of which cannot be seen. Representations can be static or dynamic and include diagrams, graphs, pictures, physical models, animations, simulations, and others” (Kozma and Russell, 2005, p. 123; Schönborn and Anderson, 2010, p. 347). Once the interviewer ensured that the topic of the interview was clear, participants were asked eight interview protocol questions, along with any related follow-up questions to better explore, probe, and substantiate the meaning of participants’ responses. The interview protocol contained the following core questions:

1. What representations do you use in your course?

2. What is the purpose of the representations that you use in your course?

3. Where do you obtain the representations that you use in your course?

4. What criteria do you use to select representations for your course?

5. When introducing new representations in your course, what strategies do you use to make sure that your students understand them?

6. Are there any skills that you want your students to develop when they learn with representations?

7. How do you know that your students understand the representations that you introduce in your course?

8. As an instructor, what resources, support, or instructional materials do you wish you had access to that would help you teach with representations more effectively?

Data analysis

Once transcribed verbatim, the interviews were analyzed in five phases (Fig. 1). A combination of inductive and deductive coding methods was used to analyze the data. The two authors collaboratively analyzed the data in each phase of the analysis. All phases of the analysis were accompanied by writing reflective memos to capture researchers’ thoughts about the data. Writing of memos helped with the communication between the investigators about the coding process, how the process of inquiry is taking shape, and the emergent patterns, categories, and themes in the data (Birks et al., 2008; Saldaña, 2013). This careful record of memos ensured confirmability and dependability of the analysis (Shenton, 2004; Anney, 2014) and the frequent debriefing sessions between the researchers helped address any biases and assumptions brought to the interpretive analysis and ensured the credibility of the results (Lincoln and Guba, 1985; Pandey and Patnaik, 2014). Additionally, the researchers discussed every case of disagreement on coding until 100% inter-rater agreement was reached (Saldaña, 2013).
image file: d0rp00329h-f1.tif
Fig. 1 Summary of the data analysis procedures.

Phase I: The goal of the first phase was to develop a coding rubric. To meet this goal, both authors read half of the transcripts to elicit emergent, data-driven (inductive) codes. Two approaches were used to develop code names: wording used by the participants themselves in the interview (in vivo codes) and codes constructed by the researchers to summarize a common idea expressed by the participants (constructed codes). First, we employed the Initial Coding method (also known as Open Coding) to break data into segments to examine their comparable commonalities, differences, and relationships (Corbin and Strauss, 1990; Saldaña, 2013). This method provided an opportunity to reflect deeply on the contents and nuances of the data and to explore all possible directions suggested by the initial examination and interpretation of the data. Second, we used the Process Coding method (also known as Action Coding) to capture strategies that instructors use to help their students develop RC. This method uses gerunds (words ending with “-ing”) “to connote action in the data” (Saldaña, 2013). This coding method helped us capture the following example codes to describe what instructors are doing in the classroom to help students develop RC skills: “scaffolding important features of representations,” “introducing simple representations first and building complexity over time,” and “explaining how scientists came up with specific representations.”

Phase II: The goal of the second phase of the analysis was to code the entire data corpus. To accomplish this goal, all transcripts were uploaded into NVivo 12 to be stored, coded, and organized (Bazeley and Jackson, 2013; QSR International Pty Ltd, 2015). During this phase of the analysis, new codes were identified and added to the codebook developed in the first phase of the analysis. In addition to coding and identifying new codes, we simultaneously engaged in Axial Coding. Axial Coding is a “category-building” method, which was used to critically examine all of the codes to combine similar codes, delete redundant codes, further examine the relationships between the codes, and build a hierarchy of categories and codes (Corbin and Strauss, 1990; Saldaña, 2013). As an outcome, a second version of the codebook was generated that was used by both authors to re-code the entire data corpus.

Phase III: The goal of the third phase of the analysis was to deductively code the entire data corpus using Kozma and Russell's Representational Competence Framework (Kozma and Russell, 2005). In this phase, we employed the Provisional Coding method, or coding of the data with a provisional list of codes generated on the basis of this study's conceptual framework (Saldaña, 2013). The following list of codes adapted from Kozma and Russell (2005, p. 132) was used in this phase of the analysis:

• Ability to interpret representations.

• Ability to generate representations.

• Ability to translate between representations.

• Ability to understand the affordances and limitations of representations.

• Ability to choose the most appropriate representation for a particular purpose.

• Ability to use representations to make predictions, draw inferences, support claims, and/or to solve problems.

Application of these codes allowed us to identify RC skills (a) that instructors aim to help students develop, (b) skills that they describe to teach, and (c) skills that they describe to assess.

Phase IV: The goal of the fourth phase of the analysis was to identify similarities and differences in the response patterns of the participants. In this phase of the analysis, we generated summaries for each instructor on the basis of all of the codes applied to analyze their transcripts (see Appendix 1). Each summary addressed the following components:

• RC skills that they aim to help students develop.

• Described teaching strategies that they use to help students develop target skills.

• RC skills that they describe to assess.

• Resources, support, and instructional materials that the instructors identified as lacking but desired to more effectively develop student RC skills.

Phase V: The goal of the final phase of the analysis was to form themes that synthesized the central ideas in the data, which “consist of all of the products of analysis condensed into a few words that seem to explain what ‘this research is all about’” (Strauss and Corbin, 1998, p. 146). The identification of the major themes required reflection on all of the codes, categories, and analytic memos written by the researchers, and an integration and synthesis of the results obtained from the previous phases of the analysis. Four themes were identified through this analysis and the results section that follows is organized around these themes.

Limitations

This study provides the first qualitative exploration of chemistry instructors’ intentions toward developing student RC skills in a variety of courses. Despite the very diverse body of research participants in our study (instructors from 11 universities across 10 different states in the US), the sample size is small (N = 13). Therefore, we do not make any generalizability claims.

Second, despite eliciting rich data, interviews were the only data collection method used in this study. Therefore, all inferences about how participants teach about representations and how they assess student understanding of representations were made solely based on participants’ reflections on their instructional and assessment practices. This single mode of data collection limited our ability to make definitive claims about participants’ teaching and assessment practices related to RC. For example, in the absence of an analysis of classroom observations and course artifacts it is impossible to make claims about the quality of how instructors teach and assess RC. Despite these limitations, capturing instructors’ thinking is nonetheless very important because teacher thinking and beliefs serve as a filter for making decisions about learning goals, instructional strategies, and content organization (Gess-Newsome et al., 2003; Gess-Newsome, 2015). Previous studies have established that chemistry instructor's beliefs (as captured through interviews and/or surveys) align with their instructional choices (as captured through analyzing classroom observations and/or surveys) (Gibbons et al., 2018; Popova et al., 2020). These findings suggest that instructors’ descriptions of their teaching with representations might shed some light onto their instructional and assessment practices.

Results

There is a misalignment between representational competence skills that instructors aim to help students develop and the skills that they report to teach and assess

Deductive coding with the Kozma and Russell's framework (2005) (Phase 3 of the analysis) was used to analyze (1) participants’ descriptions of the RC skills that they aim to help students develop, (2) descriptions of the instructional strategies that they use to teach students how to make sense of representations, and (3) descriptions of how they assess student acquisition of the desired skills. Fig. 2 depicts the results of this analysis. As can be seen, all instructors in our study (n = 13) described teaching (green in Fig. 2) students how to interpret representations; most also described teaching students how to generate representations (n = 9) and how to translate between representations (n = 10). Very few participants reported teaching other RC skills. Regarding assessment (red in Fig. 2), the ability to generate representations was the skill reported as the most commonly assessed (n = 9), followed by the ability to interpret representations (n = 6). The rest of the RC skills were reported to be assessed by very few instructors. Finally, when asked whether there are any RC skills that they want their students to develop in their courses (dark blue in Fig. 2), very few participants (n = 5) articulated any specific skills. Of these few participants, all (n = 5) reported that they aim to help students develop an ability to interpret representations, and only 1–2 instructors identified other RC skills as target skills in their courses. The remaining instructors in our study (n = 8) reported that they do not aim to help students develop any skills. Instead, they stated that they only focus on helping students develop an understanding of concepts, mechanisms, and disciplinary principles.
image file: d0rp00329h-f2.tif
Fig. 2 RC skills that instructors aim to help students develop and the skills they describe to teach and assess. “Interpret” stands for the ability to interpret representations, “generate” for the ability to generate representations, “translate” for the ability to translate between representations, “use” for the ability to use representations to make predictions, draw inferences, support claims, and/or to solve problems, “aff./lim.” for the ability to understand affordances and limitations of representations, and “choose” for the ability to choose the most appropriate representation for a particular purpose.

As can be seen, there is an apparent misalignment between the skills that instructors aim to help students develop and the skills that they report to teach and assess. For example, when explaining how they help students make sense of representations, all instructors described practices that help students develop an ability to interpret representations. Yet, only five participants explicitly identified the ability to interpret representations as a target skill that they aim to help students master. This finding suggests that some instructors might teach and assess RC skills without realizing it. Based on our data, there are several possible explanations for this discrepancy. Some participants stated that they focus on helping students develop conceptual understanding only. When asked about what RC skills they aim to help students develop, these instructors ignored that the question prompted them to discuss skills associated with understanding and using representations. P3, for example, discussed the importance of understanding various analytical techniques and instruments: “One of the goals is they need to understand how this technique works and then to choose what technique to use. The second skill requires a little bit higher level. We try to compare this technique versus another existing thing. So for example, we actually compare different types of microscopes.” As can be seen, P3 was unable to articulate any RC skills that he aims to help students develop, even when asked additional follow-up questions. In contrast to P3, P7 explicitly stated that he does not aim to help students develop any skills: “I don’t aim to develop any skills. I just think of them [representations] as tools to our conceptual understanding.” Even though P7 responded that he does not aim to develop any skills, the ability to use representations as tools to understand concepts is in itself an RC skill (i.e., the ability to use representations to make predictions, draw inferences, support claims, and/or to solve problems). It is evident that P7 did not recognize this as a skill that he helps his students develop. As can be seen, some instructors did not specify any RC skills because they were simply unaware of this skillset and the impact it has on student success in chemistry. This finding was also evident from the following quotes: “this is the first time I'm really thinking about this [teaching with representations], I just sort of did it” (P7) and “I guess I don't think about representations that much when I teach my course” (P10).

Instructors have various intentions toward developing, teaching, and assessing student representational competence skills

As part of the fourth phase of the analysis, we generated summaries for each participant on the basis of all of the codes applied to analyze each interview (see Appendix 1 for example coding summaries for P13 and P5). These coding summaries were used to compare the responses of our research participants. Upon examination of these summaries, we noticed some patterns based on the interplay between the skills that instructors aim to help students develop (concrete learning goals for developing RC), the strategies that they describe to use in the classroom to support the acquisition of these skills (instructional practices), and the focus of their assessments (assessment practices). To explore these patterns in more detail, the data were re-organized using a table format to map and compare the codes assigned for each participant (see Appendix 3). As an outcome of this analysis, we identified three distinct patterns in instructors’ responses, which allowed us to assign them into three groups. As can be seen in Fig. 3, the five participants who comprise Group 1 (P12, P13, P4, P1, and P9) expressed that they aim to help students develop one or several RC skills (e.g., were able to express concrete learning goals for developing RC). Their descriptions of the strategies that they use in the classroom when teaching about representations revealed that their instructional practices are mostly in alignment with the skills that they aim to help students develop. These instructors also reported that they assess student mastery of these skills. Five participants from Group 2 (P7, P2, P10, P8, and P6) reported that the development of student RC skills are not learning goals that they set for their courses. At the same time, their descriptions of their instructional and assessment practices revealed that, without realizing it, they are likely helping students develop several RC skills and are likely assessing the acquisition of some of these skills. Finally, the three other participants that form Group 3 (P5, P11, and P3) reported that they neither aim to develop any RC skills, nor do they intend to assess any RC skills. At the same time, their descriptions of their instructional strategies revealed that, without realizing it, these instructors are likely using strategies that help students develop some RC skills.
image file: d0rp00329h-f3.tif
Fig. 3 Patterns in instructors’ responses in regard to the number of RC skills that each participant aims to help students develop (dark blue), as well as the number of skills that each is likely teaching (red) and assessing (green), as discerned from participants’ descriptions of their instructional and assessment practices.

Once the three groupings of instructors were identified, we examined their responses in more detail. Table 2 summarizes and compares the response patterns of the three groups about their intentions to develop, teach, and assess student RC skills. Table 3 contains representative quotes that support our findings depicted in Table 2. The selected quotes are (a) illustrative, providing explicit examples that support categories and themes identified in this study, (b) succinct, expressing the same point as other, much longer quotes, and (c) representative, remaining true to the overall sentiment in instructors’ descriptions about how they teach with representations (Lingard, 2019; Eldh et al., 2020). Note that the wording “aims to develop” refers to the explicit learning goals that the instructors articulated for developing RC, whereas phrases “describes/reports to teach” and “describes/reports to assess” refer to instructional and assessment practices that they described during the interview when explaining how they teach with representations and how they assess student understanding of representations.

Table 2 Summaries of the three groups of instructors and their intentions toward developing, teaching, and assessing student RC skills
image file: d0rp00329h-u2.tif

Table 3 Representative quotes from the three groups of instructors about their intentions toward developing, teaching, and assessing student RC skills
image file: d0rp00329h-u3.tif

All three groups are represented by both males and females teaching a variety of chemistry courses. Appendix 2 summarizes demographic information of the instructors in the three groups. No distinct demographic differences were observed between the three groups, except that Group 3 is composed of assistant professors only, whereas Groups 1 and 2 are composed of both assistant and associate professors. When comparing Group 1 and Group 2, Group 1 instructors, on average, have more teaching experience. Finally, Group 1 was composed of instructors teaching undergraduate courses only, whereas Groups 2 and 3 were composed of instructors teaching both, undergraduate and graduate courses. Due to the small sample size, we are unable to make any definitive conclusions about the interplay between demographic parameters and instructors’ group standing. Below is a detailed comparison of the three groups of instructors regarding their intentions toward developing, teaching, and assessing student RC skills.

Instructors in Group 1 recognize that students need support in the development of RC skills. All of them identified and articulated at least one RC skill that they aim to help students develop in their courses (e.g., P1 identified 1 RC skill, whereas P13 articulated 4 RC skills). Collectively, the five instructors in this group identified all six RC skills as important skills that they hope to help students develop. In comparison to Group 1, instructors in Groups 2 and 3 failed to identify and describe any RC skills that they aim to help students develop. Participants in Groups 2 and 3 reported that their focus is on the development of student conceptual understating only, not on the development of skills.

Regarding the descriptions of how instructors support students when introducing new representations in their classroom, instructors in Group 1 described teaching 2–5 RC skills (e.g., P4 described teaching 2 RC skills, whereas P13 described teaching 5 RC skills). Collectively, based on their descriptions of their instructional practices, Group 1 instructors are likely to teach five out of six RC skills. The “ability to choose the best representation for a particular purpose” was not discussed by Group 1 participants, although they mentioned that they assess this skill on their exams. In contrast to Group 1, instructors in Group 2 described teaching 1–4 RC skills (e.g., P6 described teaching 1 skill, whereas P2 described teaching 4 skills). Collectively, the Group 2 instructors are likely to teach the same five RC skills as instructors in Group 1. Finally, Group 3 participants described teaching 2–3 RC skills (e.g., P3 described teaching 2 RC skills, whereas P11 described teaching 3 RC skills). In contrast to participants in Group 1 and 2, collectively, instructors in Group 3 are likely to teach only three RC skills.

Big differences were observed regarding the RC skills that the instructors in the three groups described to assess on their exams (Table 2). Group 1 described assessing 2–4 RC skills (e.g., P13 described assessing 2 skills, whereas P12 described assessing 4 skills). Collectively, participants in this group are likely to assess all six RC skills. In contrast to instructors in Group 1, Group 2 instructors described assessing only 1–2 RC skills (e.g., P8 described assessing 1 RC skill, whereas P2 described assessing 2 RC skills). Collectively, Group 2 participants are likely to assess only 2 RC skills. Finally, instructors in Group 3 reported that they do not assess any skills on their assessments. Instead, they reported that they test students upon their knowledge of concepts and disciplinary principles only.

Finally, we analyzed the alignment between the skills that instructors aim to help students develop and the skills that they are likely to teach and assess based on the descriptions of their instructional and assessment practices. For example, P9 (Group 1) reported that she aims to help students develop (1) an ability to interpret representations and (2) an ability to use representations to make inferences, draw conclusions, and solve problems. Based on the descriptions of her classroom practices, she is likely to teach students how to (1) interpret representations and (2) generate representations. Finally, she reported that she assesses students’ ability to (1) interpret representations and (2) generate representations. For the purposes of this analysis, P9 received an alignment score of 2, because two skills were aligned across two or more domains: the ability to interpret representations was the skill that this instructor aims to help students develop and described to teach and assess, and the ability to generate representations was the skill that this instructor described to teach and assess. Group1 showed the highest alignment between their learning goals for student development of RC skills and the skills that they described to teach and assess, with 2–4 skills aligned for each instructor. In comparison, only 1–2 RC skills were aligned between the skills that participants in Group 2 described to teach and assess. The alignment analysis was not possible for Group 3 since instructors in this group reported that they do not aim to help students develop any RC skills, nor do they assess any RC skills.

Instructors who are intentional about developing student representational competence skills place a higher emphasis on scaffolding these skills in the descriptions of their instructional practices

Analysis of instructors’ descriptions of their teaching practices when helping students make sense of new representations showed that all instructors in our sample are likely to teach their students some RC skills (green color in Fig. 3). To be precise, one participant (P6) described teaching 1 RC skill, four participants (P3, P8, P9, and P4) described teaching 2 RC skills, four participants (P11, P5, P10, P1) described teaching 3 RC skills, three participants (P12, P7, and P2) described teaching 4 RC skills, and one participant (P13) described teaching 5 RC skills. No differences were observed in the number of RC skills described to be taught by the participants in the three groups (instructors in the three groups described teaching, on average, about 3 RC skills).

When reading through all the quotes, we could sense differences in how the participants described their instructional practices around RC. To discern these differences, we identified in NVivo all quotes that were coded under the code “RC skills described to be taught” and analyzed each quote one more time using a coding rubric that we developed for this analysis. The inductive coding rubric included three codes: (1) demonstrates the skill – to code quotes in which the instructor described showcasing RC skills, without providing any descriptions of how they scaffold these skills to support student mastery; (2) demonstrates and scaffolds the skill – to code quotes in which the instructor described not only showcasing particular RC skills, but also providing their students with a step-by-step explanation of the skills; or (3) provides opportunities for students to practice the skill – to code quotes in which the instructor described not only modeling RC skills, but also encouraging their students to practice the skills. Table 4 shows example applications of these codes to our data.

Table 4 Analysis of representative quotes to demonstrate the differences in instructors’ descriptions of how they teach RC skills (in this specific example, the ability to translate between representations). The probing questions from the interviewer are shown in grey and italic. “I” stands for the “interviewer”
image file: d0rp00329h-u4.tif

As can be seen in Table 4, P7's response was coded as “demonstrates the skill” because, even with probing, we were not able to elicit any descriptions of this participant helping students to map features of one representation (2D drawing) onto features of another (3D model). According to the interview data, this participant just shows his students ethylene polymerization as a 2D drawing and as a 3D molecular model and leaves it up to the students to make the appropriate connections between these representations. In comparison, P8's description of how he helps his students translate between a static cartoon and a dynamic animation of ATP synthase was coded as “demonstrates and scaffolds the skill.” As can be seen, P8 first carefully scaffolds features of the ATP synthase's cartoon – “I talk about the different parts of the enzyme and which parts rotate and how they rotate and which parts don't rotate and how ATP is made.” Then, when introducing students to the corresponding animation, he intentionally maps features of the animation onto features of the cartoon – “I'll even pause the video and stop and point out something on the structure, then replay it.” Finally, P1's description of how she helps students make connections between different representations was coded as “provides opportunities for students to practice the skill” because she not only helps students “connect the representations to each other,” but she also asks students to conceptually engage with the representations to explain the ways in which they are similar and different from each other.

Similar coding was performed with the rest of the quotes under the code “RC skills described to be taught.” A total of 48 quotes were analyzed with the developed rubric. Fig. 4 shows the output of this analysis, as well as a comparison of how instructors in the three groups described to teach RC skills. In the absence of classroom observations data it is impossible to make definitive claims about how instructors teach RC skills. However, given that chemistry instructors’ thinking and beliefs align with their instructional choices (Gibbons et al., 2018; Popova et al., 2020), instructors’ descriptions of their teaching practices are likely shedding some light on their instructional strategies around teaching RC skills.


image file: d0rp00329h-f4.tif
Fig. 4 Comparison of participants’ descriptions of how they teach RC skills.

Approximately 13% of all quotes were coded as “demonstrates the skill,” 57% of quotes were coded as “demonstrates and scaffolds the skill,” and 30% of quotes were coded as “provides opportunities for students to practice the skill.” Fig. 4 illustrates the differences between approaches to teaching RC skills described by instructors in the three groups. As can be seen, no quotes from Group 1 instructors were coded as simply “demonstrates the skill.” This analysis supports and augments our previous findings that Group 1 participants not only aim to help their students develop RC skills (Fig. 3), but they also likely do so in a thoughtful and intentional way. In contrast, 20% of quotes from Group 2 and 25% of quotes from Group 3 participants were coded as simply “demonstrates the skills.” Given our previous findings that instructors in these two groups reported that they do not aim to develop student RC skills, it is unsurprising that some of their quotes reflected a lack of support and scaffolding when teaching about representations.

Most instructors expressed interest in professional development targeting effective teaching about representations

During the interviews, four participants admitted that they do not know much about student learning with representations. For example, P7 stated that he is not familiar with any literature on the topic: “I’m sure there's probably much better ways [to teach about representations] that are described in the educational literature, but I don’t really dig much into that.” P13 shared a similar sentiment: “In terms of actual chemistry education, or education research in terms of how students think, I don’t know much about that… There was never anything stopping me from looking this up before. And clearly, I haven’t done it. But yes, I think it certainly behooves us to be well informed in what, in what we’re doing.”

In addition to some instructors reporting self-awareness about their lack of knowledge about evidence-based approaches to teaching about representations and developing student RC, eight participants expressed a desire for professional development opportunities to learn about evidence-based practices for teaching and learning with representations. These eight instructors included all instructors from Group 1 and three instructors from Group 2. These instructors reported that it would be useful to learn about how to effectively teach about representations, how to assess student understanding of representations and mastery of the RC skills, as well as about differences in how experts and novices conceptualize representations (Table 5). Some participants expressed a desire to learn about two of the aforementioned three aspects. Six instructors also reported that they would like to learn about existing sources of representations and how to locate specific representations (e.g., animations, simulations). Finally, no instructors from Group 3 expressed an interest in professional development opportunities to learn about evidence-based approaches to teaching about representations. For example, P11 stated that he does not need any support or resources; instead, he expressed that lack of time is a barrier to provide effective instruction around chemistry representations: “A big part of the problem is the volume of material expected to be covered. We can’t spend a lot of time on different representations, going over them in detail, because of the volume of content. There's an expectation that in two semesters we'll cover basically the whole textbook and that equates to one chapter per week approximately. And that dictates how much time we can spend on any one representation. So I'd actually say that I don't think there's a lack of resources. I think there's a lack of time to utilize those resources.” Lack of time is a well-documented barrier for educational change and classroom experimentation (Shadle et al., 2017).

Table 5 Instructors’ descriptions of the resources and professional development support that they need to teach about representations more effectively
Code n Representative quote
Finding existing resources 6 P12: “I need some more professional development so I can learn about what's out there. Like I know there's stuff out there, like animations, but I just don't have the time to find it. Then learning about them [representations] and then how to apply them to my class, um, are all like part of the struggle. Like if there are resources out there, like what are they, and how do I find them, and how do I use them?”
Effective teaching about representations 5 P7: “Having somebody who's actually dug into the literature and said, here's how you teach students to interpret a new kind of graph would be very helpful. Because maybe the pick one data point thing I've latched onto is useful [for context, see P7's quote in Table 3, under “description of the taught RC skills”]. Maybe it's a total junk. I don't know. Um, so it'd be nice to have someone who actually knows about these things to instruct on that.”
Assessment of understanding of representations and mastery of the RC skills 2 P4: “A really useful workshop will be one to show how representations could be used, particularly how you could utilize them, like in an exam setting. So assessing both, you know, [students’] conceptual understanding as well as, you know, in chemistry, certain things like generating data and plots and stuff are important skills that students need to have. So ways to incorporate it into an exam to test their ability to generate those types of models or representations.”
Expert-novice differences in understanding representations 2 P9: “I think that if there was some sort of workshop or resource to think about using the visual representations, that would definitely be helpful. A workshop to push us [instructors] to think about how students really interpret representations, right? Especially if you’ve been at kind of the expert level in the field for a long time, those representations become just second nature and it might be hard for people to remember how you looked at them the first time or how different students struggle to try to conceptualize their knowledge. And it might be different from how the expert is interpreting that image.”

Conclusions and discussion

This study aimed to identify and characterize chemistry instructors’ intentions toward developing, teaching, and assessing student RC skills. We found that the ability to interpret representations, generate representations, and translate between representations were the skills described to be taught by most instructors in our study. The data, however, shows a lack of a focus from our participants on developing student meta-representational skills (e.g., the ability to choose the most appropriate representation for a particular purpose, the ability to describe affordances and limitations of representations). Similarly, Linenberger and Holme (2015) found that biochemistry instructors identify the ability to interpret and generate representations as the key skills for their courses and Xue and Stains (2020) found that one of the two instructors in their study focused on meta-representational competence, whereas the other did not. These findings suggest a gap in the current instruction because according to prior research, it is important to go beyond simple skills such as the ability to interpret representations and support students in the acquisition of higher-level meta-representational competence (diSessa, 2004; Schönborn and Anderson, 2006; Xue and Stains, 2020). In regard to assessment, the ability to generate representations was a skill described to be assessed by most instructors in our study and the ability to interpret representations was a skill described to be assessed by about half of our research participants. There was a very low emphasis on assessing other RC skills. Additionally, 38% of instructors in our study reported that they do not assess any RC skills. Our findings somewhat align with the findings of Linenberger and Holme (2015) who found that 57% of biochemistry instructors assumed these skills on their assessments, indicating that they were not directly assessed. As can be seen, both studies show that some instructors report not assessing representational competence or visual literacy in their courses. Finally, very few instructors (n = 5) articulated RC skills that they intended to help their students develop. The rest of the participants in our study (n = 8) reported that they do not aim to help students develop any RC skills.

Our findings clearly suggest that there is a misalignment between RC skills that instructors aim to help students develop and skills that they describe to teach and assess. This means that some instructors are likely to teach and assess RC skills without realizing it. Based on our data, there are two possible reasons for this discrepancy. Some participants reported that they focus on helping students develop conceptual understanding only and that the development of domain-specific skills is not something that they recognize as important to teach. Other instructors did not specify any RC skills because they were simply unaware of this skillset. An instructor cannot effectively support their students in the acquisition of skills that the instructor is unaware of. Unless specific skills are identified as learning goals in a course, it is unlikely that these skills will be effectively taught and assessed (Stowe and Cooper, 2017). Our analysis depicted in Fig. 4 supports this claim. There is a clear difference between approaches to teaching RC skills described by the instructors in the three groups. We found that Group 1 participants who were intentional about developing student RC, described scaffolding RC skills in their classrooms and providing students with opportunities to practice these skills. In contrast, those who reported that they do not aim to help students develop any RC skills were not always able to describe how they scaffold the RC skills in their courses, with 20% of quotes from Group 2 and 25% of quotes from Group 3 coded as simply “demonstrates the skills,” suggesting lack of support when teaching about representations.

Implications

To effectively transform chemistry education, it is necessary to forge professional development programs to equip instructors with literature-based resources and support that could meaningfully affect instructors’ pedagogical knowledge and teaching practices (Gess-Newsome et al., 2003; Henderson et al., 2011; Talanquer, 2014). Some instructors in this study reported self-awareness about their lack of knowledge about developing student RC. In addition, the majority of the participants expressed a desire for professional development opportunities to learn about differences in how experts and novices conceptualize representations, about evidence-based practices for teaching about representations, and about assessment of student understanding of representations and mastery of RC skills. We also found that those who are aware of RC skills and are intentional about developing student RC skills are more cognizant of gaps in their knowledge and of areas for potential professional growth. In contrast, those who were unaware of RC did not express any desire to learn about this construct because, as the saying states, “we don’t know what we don’t know.” Finally, given that we interviewed chemistry instructors specifically (and not other STEM instructors) about teaching and learning with representations, it is perhaps surprising that no participant has mentioned the importance of helping students navigate between different corners of the Johnstone triangle (i.e., symbolic, microscopic, and submicroscopic) when learning about representations (Johnstone, 2006, 2010; Taber, 2013). Given that much of the scholarship in chemistry education research draws upon Johnstone's triplet and recognizes this model as central to teaching and learning about chemistry, it is concerning that the thirteen chemistry instructors in this study were unaware of this model as evidenced by the absence of a single quote discussing these ideas.

This study holds clear implications for informing faculty professional development initiatives. Instructors need effective pedagogical training tailored to teaching about domain-specific representations. Such pedagogical training needs to help instructors (1) take cognizance of relevant theories of learning such as constructivism (Novak, 1993), dual-coding theory (Paivio, 1986), and the information processing model (Baddeley, 2003)), as well as how to facilitate meaningful learning with chemical representations (e.g., Johnstone's triangle (Johnstone, 2010)), (2) learn about the key factors affecting students’ ability to reason with representation (e.g., prior content knowledge, nature and mode of external representations, understanding of the visual language and conventions used by various external representations) (Schönborn and Anderson, 2006, 2010), (3) foster awareness of RC skills and how to support students in the development of both lower-level skills and higher-level meta-representational skills (diSessa, 2004; Kozma and Russell, 2005), and (4) familiarize instructors with the use of representations to promote equitable assessment design (Ralph and Lewis, 2020). Institutions and departments need to incentivize and reward participation in such professional development efforts, as well as create more platforms for chemistry instructors to reflect on their teaching in a meaningful way and to engage with the research literature on student learning about chemical representations. The success of potential future faculty professional development initiatives around RC needs to be studied to establish an understanding of what constitutes effective training in this domain.

There are multiple avenues for future research in this area. First, future studies need to more thoroughly capture how instructors teach and assess student RC skills. Triangulation of data collection methods (e.g., interviews, video observations of teaching practices, and analysis of assessments) would allow for richer and more nuanced characterization of instructional and assessment practices. Second, future research needs to characterize the impact of instructors’ thinking and teaching practices on student development of RC skills. This analysis would include triangulation of faculty interviews, classroom observations, and student learning outcomes. However, such an investigation requires a high-quality assessment to measure student learning outcomes related to their RC. Despite the considerable number of investigations into students’ ability to reason with representations (Kozma and Russell, 1997; Seufert and Brunken, 2004; Cooper et al., 2010; Mccollum et al., 2014; Stieff et al., 2016; Popova and Bretz, 2018a, 2018b, 2018c), no assessment instrument currently exists in chemistry that captures and measures student RC. Therefore, an assessment instrument is needed to measure student RC skills in the context of chemical representations. Indeed, this gap was highlighted by Kozma and Russell who noted that “new assessments must be designed and used that measure investigation practices and related skills, such as visualization skills or representational competence” (Kozma and Russell, 2005, p. 142).

Conflicts of interest

There are no conflicts to declare.

Appendix 1

Example coding summaries for P13 (Group 1) and P5 (Group 3)
image file: d0rp00329h-u5.tif

Appendix 2

Demographic information of the three groups of instructors
image file: d0rp00329h-u6.tif

Appendix 3

Data summary used to identify patterns in the skills that instructors aim to help students develop, skills that they describe to teach, and skills that they describe to assess (first table), as well as the product of this analysis in which instructors are grouped based on the observed patterns (second table). “No RC skills” stands for no representational competence skills, “interpret” stands for the ability to interpret representations, “generate” for the ability to generate representations, “translate” for the ability to translate between representations, “afford. and limitations” for the ability to understand affordances and limitations of representations, “choose” for the ability to choose the most appropriate representation for a particular purpose, and “use” for the ability to use representations to make predictions, draw inferences, support claims, and/or to solve problems.
image file: d0rp00329h-u1.tif

Acknowledgements

We thank the chemistry instructors who participated in this study. We also thank M. Stains for the help with recruiting study participants, the Popova research group members for the help with transcribing and analyzing interviews, and N. Baldwin for her feedback on an earlier draft of this article. Finally, we thank the reviewers and the editor for their thoughtful comments and valuable efforts toward improving this manuscript.

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