The complexity of chemistry mindset beliefs: a multiple case study approach†
Deborah L. Santos
ab and
Suazette Reid Mooring*b
aGeorgia Institute of Technology, Atlanta, USA
bGeorgia State University, Atlanta, USA. E-mail: smooring@gsu.edu
First published on 9th July 2024
Abstract
Mindset is a construct of interest for challenging learning environments, as science courses often are, in that, it has implications for behavioral responses to academic challenges. Previous work examining mindset in science learning contexts has been primarily quantitative in nature, limiting the theoretical basis for mindset perspectives specific to science domains. A few studies in physics education research have revealed domain-specific complexities applying to the mindset construct that suggest a need to explore undergraduate perspectives on mindset within each science domain. Here we present a multiple case study examining chemistry-specific mindset beliefs of students enrolled in general and organic chemistry lecture courses. A between-case analysis is used to describe six unique perspectives on chemistry mindset beliefs. This analysis revealed that students’ beliefs about their own ability to improve in chemistry intelligence or regarding chemistry-specific cognitive abilities did not consistently match their views on the potential for change for other students in chemistry. The nature of the abilities themselves (whether they were naturally occurring or developed with effort), and the presence of a natural inclination toward chemistry learning were observed to play a role in students’ perspectives. The findings from this analysis are used to propose a more complex model for chemistry-specific mindset beliefs to inform future work.
Introduction
General and organic chemistry undergraduate courses present a variety of challenges to many students and thus often require persistence to achieve successful outcomes. Some students manage to overcome their challenges, while others do not. General and organic chemistry are considered gateway courses for various science, technology, engineering, and math (STEM) degrees, as well as prerequisites for many professional programs (Tai et al., 2005; Koch, 2017; Harris et al., 2020). Course failure and withdrawal rates are typically high for general and organic chemistry, not unlike other STEM gateway courses (Horowitz et al., 2013; Popejoy and Asala, 2013; McKinney et al., 2019; Harris et al., 2020). These rates of unsuccessful course completion support the claim that academic challenges are both plentiful and present substantial obstacles for many students. Institutions aim to increase STEM course retention, increase course completion success rates, and contribute to increased diversity in STEM and medical professional fields. Insight into the nature of students’ decisions to persist or give up in the face of challenge is instrumental to addressing this problem.Mindset has been identified as a relevant psychological construct to include in the investigation of persistence behaviors as it involves beliefs about the malleability of intelligence and is linked to persistence and challenge-seeking behaviors (Molden and Dweck, 2006; Doron et al., 2009; Burnette et al., 2013; Lou and Noels, 2016; Karlen et al. 2019). The term “mindset” originates from research on Implicit Theories of Intelligence, which states that individuals hold either incremental theories (beliefs that intelligence can increase) or entity theories of intelligence (beliefs that intelligence is a fixed trait). Incremental theories are linked to persistence because improvement is believed to be achievable with effort. Entity theories are linked to giving up because challenges are believed to be associated with evidence that one's intelligence is insufficient for the task (Dweck and Leggett, 1988). The terms “growth mindset” and “fixed mindset” are more commonly used in more recent studies but are still based on the original definitions of “incremental” and “entity” theories of intelligence, respectively (Lüftenegger and Chen, 2017).
In recent years, there has been an increasing interest in understanding mindset in STEM contexts (Gorson and O'Rourke, 2019; Little et al., 2019; Limeri et al., 2020a; Limeri et al., 2020b; Lytle and Shin, 2020; Morris et al., 2019; Kalender et al., 2022). This increased research interest has been provoked by findings suggesting that student beliefs about specific domains vary and are more predictive of their outcomes in that domain relative to their general mindset beliefs (Shively and Ryan, 2013; Scott and Ghinea, 2014; van Aalderen-Smeets et al., 2019). Gender and racial stereotypes likely influence students’ beliefs about who can succeed in certain STEM fields, and thus their field-specific mindset beliefs (Aronson et al., 2002; Good et al., 2003; Burkley et al., 2010; Good et al., 2012; Leslie et al., 2015; Lytle and Shin, 2020; Ibourk et al., 2022). Several studies have found that domain-specific mindset beliefs: (1) decline over time in STEM courses, and (2) are more predictive of student outcomes relative to general mindset beliefs (Shively and Ryan, 2013; Dai and Cromley, 2014; Scott and Ghinea, 2014). These findings highlight the profound effects of experiences in STEM courses and the importance of understanding students’ beliefs in association with these domains.
Most mindset theory development occurred through an examination of phenomena in young children to explain behavioral differences (Dweck and Leggett, 1988; Macakova and Wood, 2020). There is evidence to suggest that not only are mindset beliefs at the undergraduate level more complex relative to younger students, but also that the domain-specificity becomes more relevant as students age (Gunderson et al., 2017). Gunderson and coworkers found that students' beliefs about their peers’ ability in math become less growth relative to their beliefs about their peers’ ability in language with increasing age (2017). These belief gaps only increase when it comes to student beliefs about adults working in math-related fields compared to writing-related fields. Recent meta-analyses conducted over large samples of mindset studies have found inconsistent results for mindset interventions and the predictive relation of mindset with achievement for adult students (Costa and Faria, 2018; Sisk et al., 2018). The average effect sizes for the impact of mindset (with or without intervention) on achievement observed across studies decreased with students’ increasing age. An improved understanding of the underlying differences in the impact of adult student mindset and associated behaviors on achievement can help to differentiate these effects from those observed in younger students.
Reports that students can endorse both growth and fixed mindset beliefs simultaneously have existed since early in the mindset research (Dweck et al., 1995a, 1995b). However, the notion that mindset beliefs are context-dependent has gained traction in research lately. The learning environment can activate one view over another (Little et al., 2016), which may yield various effects on student behaviors. The shift in beliefs as a function of a performance feedback loop for STEM subjects also suggests that context matters (Dai and Cromley, 2014; Scott and Ghinea, 2014; Limeri et al. 2020a). Likewise, findings that instructors’ mindsets about students and the messaging expressed in instruction and teacher-student communication impact student outcomes also point to the environmental influences on student beliefs (Barger, 2019; Canning et al., 2019; Muenks et al., 2020; LaCosse et al., 2021). Little and coworkers called for a shift in methodology away from survey measures that capture a small snapshot of students’ views toward rich qualitative analysis to begin understanding the nature of context influences on student mindsets in physics (2016), which could be equally important in other STEM domains like chemistry.
A deeper understanding of the various aspects of undergraduate chemistry mindset perspectives is needed, along with an examination of contextual influences on the expression of these beliefs in chemistry courses. This multiple-case study examines eight students’ chemistry-intelligence beliefs and experiences in general and organic chemistry to characterize chemistry-specific mindset perspectives as indicated by their expressed beliefs, behaviors, and interpretations of challenges.
Theoretical framework
Domain-general mindset theory
Mindset research began with the discovery of behavioral pattern differences in children when faced with challenges or failures (Diener and Dweck, 1978; Dweck and Leggett, 1988). Eventually, the two opposed implicit theories about the nature of intelligence (incremental and entity theories) were uncovered as an explanatory factor for these differences in behavioral responses by way of setting differential goals (mastery versus performance goals) (Bandura and Dweck, 1985; Dweck and Leggett, 1988).The key difference between these two operating theories lies in the meaning attributed to failures or challenges (Molden and Dweck, 2006). A student who endorses incremental theory beliefs will interpret failures as challenges that have yet to be overcome because they believe their intelligence can attain the necessary level for success at a task. On the other hand, entity theorists view failures as an indicator of their insufficient ability and do not believe it is possible to affect their intelligence level. The lack of control over intelligence associated with entity beliefs yields helpless responses and negative affect when exposed to failure experiences in attempts to deflect attention from their insufficient ability. Some behaviors associated with entity theories are procrastination (Howell and Buro, 2009), reduction of effort (Burnette et al., 2013), avoiding help-seeking, evaluation, and difficult tasks (Hong et al., 1999), and minimizing the importance of the failure by changing pursuits (Molden and Dweck, 2006). In sharp contrast, the presence of a feeling of control over intelligence associated with incremental beliefs encourages efforts to improve, persistence, maintained confidence, enjoyment of challenge, and positive affect associated with minor improvements (Dweck and Leggett, 1988; Molden and Dweck, 2006).
Behavior-aligned mindset model
To align mindset theory with a process-oriented motivation theory, Burnette et al. (2013) proposed the Setting/Operating/Monitoring/Achievement (SOMA) model. The SOMA model provides a framework for operationalizing the interrelations between mindset beliefs and behaviors through a self-regulation motivation theory lens (Bandura, 1986; Carver and Scheier, 2001). The types of goals students set are associated with their implicit theory beliefs, such that mastery goals align with incremental beliefs and performance goals align with entity beliefs. Students then operate out of these goals by incorporating various mastery or helpless strategies as seen fit. The strategies a student utilizes can be affected by the presence of an “ego threat,” or a challenge, which is incorporated into the model as a mediator between implicit theories and goal operating strategies. Students monitor their progress toward goal achievement to inform future behaviors and will likely adjust their strategies to improve their goal operation. The publication that presented this model used a meta-analysis to test the hypotheses embedded in the theoretical framework using path analysis modeling techniques. The model was supported by the study results and provides a useful conceptualization for the practical behavioral and achievement outcomes of interpreting challenges as ego threats. Through this model, the helpless operating strategy is the selected response to the interpretation of a challenge as an ego threat and was found to negatively predict goal achievement (Burnette et al., 2013).A general mindset model synthesizing the literature on the interconnections between three major mindset themes (mindset beliefs, challenge experiences, and behaviors) was developed by the authors as a framework for analysis, inspired by the SOMA model. Fig. 1 presents this general mindset model as a Venn diagram. At the center of the three interacting factors in student experiences lies ego threat. Ego threat here is conceptualized as the meaning associated with challenge as a function of the beliefs that determine behavioral responses. It should be noted that the work associated with the SOMA model was conducted across domains (domain-general) and through quantitative techniques, thus lacking the specificity to academic and STEM contexts as well as the depth of qualitative investigation.
 | ||
| Fig. 1 A three-pronged mindset meaning model for case analysis indicating the interactions between mindset, challenges, and behaviors, where green represents growth mindset interpretations (productive strategies or beliefs) and actions and red represents fixed mindset interpretations and actions (unproductive strategies or beliefs). | ||
Theoretical model interpretation
For the purposes of this study, it was necessary to create operational definitions of challenge, mindset, behavior, and ego threat. The research team created definitions to apply during analysis: (a) Ego threat – an interaction of beliefs about intelligence with challenge indicating a threat to one's sense of self, producing defensive behaviors as a response. (b) Behaviors – what students do when learning, challenged, or feeling threatened (Burnette et al., 2013). (c) Challenge – the nature of the challenges students face and what they perceive to be challenging. (d) Mindset – students’ beliefs about their ability to improve in a particular area and how they view natural abilities in the context of chemistry.It is important to explain the hypotheses associated with each overlap between factors represented in the general mindset model from Fig. 1. We can begin by considering the overlap of mindset and challenge. When a challenge is present, differences in interpretations of that challenge arise as a function of mindset. A growth mindset interprets challenge as a need to increase or modify effort strategies, and a fixed mindset interprets challenge as indicative of lacking ability. Similarly, when challenge is absent, differences in interpretation are possible as functions of mindset: a growth mindset interprets the lack of challenge as a demonstration that previous effort has allowed relevant skills to be developed, while a fixed mindset interprets the lack of challenge as indicative of high or natural ability in the relevant area.
The next relationship to consider is the interaction between challenge and behavior. The interpretation of challenge as ability-related leads to helpless responses such as avoiding demonstrating ability or evaluation, sabotaging performance by other means such as procrastination, and giving up or disengaging emotionally to deflect blame on the level of caring. This ability emphasis also leads to focusing attention on negative feedback and performance outcomes. Meanwhile, the interpretation of challenge as effort-related or “needs-development” leads to mastery responses such as seeking help from other sources, altering strategies, exerting more effort, and increasing self-regulation. The effort emphasis also leads to focusing attention on improvement and the learning process.
The final relationship depicted in the model is between mindset and behavior, such that, the behavioral responses indicate the students’ mindset through practical demonstration of their beliefs. When considering students’ effort beliefs, the belief that necessary effort implies low ability reveals a fixed mindset, while the belief that effort is the means to improve at any ability reveals a growth mindset. When considering students’ willingness to change and improve, ignoring feedback as useful for improvement and decreasing effort reveals a fixed mindset, while attention to improving through feedback and increasing effort reveals a growth mindset.
Using this model, we can contrast theoretical criteria for identifying growth and fixed mindset individuals as opposite ends of a continuum. A student with a strong growth mindset believes that any ability can be developed or improved given the appropriate resources and will to do it, does not give up easily in the face of challenge, and focuses on understanding and mastery as a litmus test for success. Alternatively, a student with a strongly fixed mindset believes that abilities tend to be naturally derived and explain the differences between people in achievement and intelligence. This student will also more readily give up in the face of challenge, especially if it is the first serious challenge encountered in life and focuses on achievement and competitive measures of success. This theoretical model will be used in the data analysis of this study as a lens to recognize behavioral indicators of growth or fixed mindset beliefs.
Science domain-specific mindset theory
Recently, two studies presented a new framework for mindset in undergraduate physics contexts (Kalender et al., 2022; Malespina et al., 2022). Although chemistry and physics are different domains, both fall under the STEM field umbrella, are physical sciences, are known to have difficult courses, involve substantial mathematical and spatial reasoning abilities, and are likely to present challenges. Two dimensions were described as relevant to students’ physics mindsets, resulting in a four-component model of physics mindset: “my ability” beliefs, “others ability” beliefs, “my growth” beliefs, and “others growth” beliefs (Kalender et al., 2022; Malespina et al., 2022). This perspective allows students to endorse each belief to some degree, acknowledging that they can believe in the improvability of intelligence while simultaneously believing there are limitations to it based on natural capacities. These views of the self can match views of others or be misaligned due to high or low self-confidence in physics ability. Multidimensional modeling was used to resolve the mindset subfactors associated with a set of items developed for physics mindset with students enrolled in introductory calculus-based physics courses, revealing the four components of physics mindset. Additionally, MANOVA tests were used to uncover gender differences in mindset beliefs. Male students had significantly higher mindset scores and female students were found more likely to believe that ability is needed to be successful in physics. They also found that “my ability” (fixed physics ability about self, reverse coded) beliefs positively predicted course grade and “others growth” (improvability of physics intelligence for others) beliefs negatively predicted course grade (Kalender et al., 2022). The results do not completely align with general domain mindset findings and were also generated through quantitative methods. However, this framework provides insights and motivation for conceptualizing the nuances of mindset in a STEM domain.Study goals and design
The overarching purpose of this study is to examine chemistry mindset as a phenomenon within the context of general and organic undergraduate chemistry courses. Understanding this phenomenon requires qualitative theoretical development, which makes a multiple case study approach very valuable as a starting point. Each case will be briefly examined to consider how individual students can be classified with respect to their expressed chemistry mindset beliefs and behaviors. Comparisons will be made across cases to make distinctions between different chemistry mindset perspectives and to uncover relevant themes. Specifically, the research questions driving the study of each case and subsequent analyses are:1. How can differences in chemistry mindset be characterized considering students’ beliefs on the nature of chemistry-related abilities, interpretations of challenge, and behavioral responses?
2. What degree of alignment is observed between interview themes and extant general intelligence mindset theory to provide insight into chemistry mindset as a distinct construct?
To address these research questions, interview content will be analyzed inductively for chemistry-specific mindset content using broader themes from the general mindset model (Fig. 1).
Methods
Multiple case study design
This study serves as a qualitative component within a larger study on chemistry-specific mindset of students in general and organic chemistry courses. The study was conducted through a multiple-case study methodology. The purpose of this case study analysis was two-fold: instrumental and descriptive. An instrument case study serves to gain insight into a phenomenon through examination of each case (Stake, 1995). In this study, the phenomenon we aim to better understand is the relationship of undergraduate chemistry mindset beliefs with challenges and behaviors. The second purpose was to provide a full description of this phenomenon within context, aligning with a descriptive case study design (Yin, 2003). For this reason, the unique background of each student must be considered, along with their experiences in the course and the context in which they discussed mindset topics. In a multiple case study, the unit of analysis should be clearly defined. In this study, the unit is defined as a single student over at least two semesters. A total of 8 individual units were examined to understand the complex nature of chemistry mindset through consideration of each individual's interrelated mindset, challenge, and behaviors, and through comparison between cases to identify patterns in this science domain-specific context.Participant recruitment and case selection
The overall study was approved by the Institutional Review Board before any data collection. Participants were recruited from general or organic chemistry lecture courses via participation in a chemistry mindset survey (a focal point of the larger study, see Data sources below) at the beginning of the Fall 2020 semester (“pre-semester survey”). Students indicated at the end of the survey whether they were willing to be interviewed. Selecting students for interviews was based on their answers to survey questions, with a goal of including equal numbers of students who self-reported fixed mindset beliefs, growth mindset beliefs, and those who reported average mindset values between growth and fixed regions of the distribution. Likewise, their open-ended definitions of chemistry intelligence were considered in the selection process because many students included mindset-related statements about chemistry intelligence along with their definitions. This was used as a secondary source of evidence for their beliefs beyond their self-report on survey mindset items. A total of 14 individuals were interviewed during Fall 2020 and 5 additional students were interviewed in Spring 2021, following similar recruitment procedures. These students received $10 for their participation in the interview.During Fall 2021, only selected case study participants were invited to participate in a follow-up interview to gain additional insight into their views. For this second interview, a $20 incentive was offered to ensure high participation and reduce attrition. To select individuals for a multiple case study from the larger interview participant pool, students who had completed both pre- and post-semester surveys in Fall 2020 and Spring 2021 (a total of 4 survey time points) were identified. This criterion was used because it indicates full data existed for each of the participants selected for the case study, leading to an inclusion of eight individuals. Seven were initially interviewed in Fall 2020 and one, Camille, was interviewed in Spring 2021. The case study participant characteristics are described in Fig. 2. Two students were first-year (freshmen) undergraduates during the first interview semester, three were second-year students (sophomores), two were third-year (juniors), and one was a post-baccalaureate student completing course prerequisites for medical school admission. Three students initially participated during general chemistry I, two during general chemistry II, and three during organic chemistry I. Students who initially participated in organic chemistry II courses were not included since they were no longer enrolled in introductory courses by Spring 2021. This reason also applied to fourth-year seniors; they could not participate once they had graduated. Students in this study had a range of demographic backgrounds and previous educational experiences.
 | ||
| Fig. 2 Multiple case study participant selection with student characteristics at the time of first interview and the data sources utilized within each case. | ||
Data sources and collection
Data from each case participant were collected through a combination of survey measures, open-ended responses to survey questions, an initial interview, and a follow-up interview if they were willing to do so. Surveys were administered using online Qualtrics® survey software during the first three weeks and last three weeks of each semester, Fall 2020 and Spring 2021. Instructors provided survey access to students by posting an announcement containing a link within their course management pages. During this time, the Chemistry Mindset Instrument (CheMI) (Santos et al., 2022) was under development and each survey supported the development process through the inclusion of open-ended questions and various iterations of the instrument. Measures related to achievement goals and self-efficacy were included for purposes of the larger study.In addition to survey response data, case study participants provided in-depth interview content for analysis. The first interviews were conducted during Fall 2020 and were semi-structured. Interviews took less than one hour and incorporated questions as well as several tasks to prompt deeper discussion of mindset topics. Interviews were conducted online using a virtual meeting platform and students were sent a PowerPoint © file containing the tasks before beginning. The full interview protocol and the tasks students completed during these interviews are available in the Appendices A and B. Interviews were screen-captured and audio-recorded for later analysis. After initial questions, students were prompted to share their screens and present the slides associated with a particular task. Students were instructed on how to complete the task and told to use a think-aloud method as they completed each task. Probing questions about the reasons behind task decisions and beliefs indicated in the task were then used to elicit a deeper discussion of each student's views. At the end of the interviews, students were asked to clearly state their mindset beliefs and explain why they hold such beliefs (“Do you think that people can change their intelligence in chemistry? How did you come to believe this?”).
A follow-up interview was requested during Fall 2021 from each of the case study participants. The interview protocol is provided in the ESI.† Questions in this interview focused more on students' experiences in chemistry classes, backgrounds, challenges, responses to challenges, and perceptions of others’ views about chemistry as a subject. Towards the end of the follow-up interview, each case study participant was directed to comment on one of their previously completed tasks. This involved sharing whether they still held the views they did at the time of task completion, explaining why they think they completed it that way, and providing what (if any) changes they would make.
Qualitative analysis
All interviews were transcribed verbatim using audio recordings. The resulting text files were then imported into Nvivo® to conduct inductive coding. The coding scheme was developed using the interviews of non-case study participants from the Fall 2020 data pool. Two researchers simultaneously coded the same interview file separately and then met to discuss and negotiate codes. Interviews were analyzed for content related to mindset, challenge, and behavior and these served as the three lenses for coding rounds. The coding occurred via a three-pass method where each coder considered one lens of analysis at a time (mindset, challenge, or behavior), then repeated with each of the other two analysis lenses. Over time, new codes were identified and were merged to represent similar concepts. Additionally, the names of inductive codes were altered to align with literature mindset language. In establishing the final codebook, the two researchers repeated the simultaneous coding and discussion sessions through 6 total interviews until data saturation was reached. The definition applied to establish data saturation was taken from the method described by Guest and coworkers (2020) and considered the proportion of new codes appearing during each consecutive round of inductive content analysis. When codebook changes had slowed significantly to the point that no further changes were made when considering another data set, data saturation had been reached. Additionally, interrater reliability was examined after each interview and an acceptable Cohen's Kappa value (κ = 0.705) was obtained in the final codebook development round with analysis of Isabel's interview.One final coder meeting was conducted to refine codes further and align the names of each theme more closely with terminology from the mindset literature. Additional details of the final codebook are available as ESI,† accompanying the online article (Table S1, ESI†). Once the codebook and interrater reliability were inductively developed and established using other transcripts from the larger interview pool, all case study interviews were coded according to the three-pass method previously described. This coding scheme was applied to both initial interviews and follow-up interviews deductively.
After coding all interviews, coding frequencies were compared across cases. The transcripts were examined for relevant quotes to represent their expressed views on each aspect (mindset, behavior, and challenge), and summaries for each case were drafted. These summaries were sent to each participant for member-checking, or verification that the summaries accurately represented their views. Most participants replied that it was a correct representation or submitted minor corrections to explain in more detail.
Limitations
In qualitative analysis, bias is difficult to minimize due to personal interpretations and communication styles. To address this, member-checking was employed with students after their follow-up interview was completed. Only 6 of the 8 case participants agreed to follow-up interviews and one of these did not submit their review of the member-checking summary provided. Additionally, eliciting responses that reveal students’ views required multiple interview activities and revised questioning in the second round. It is possible that some students did not fully express their beliefs within the constraints of the interview structure. To further obscure student views, their comments about chemistry mindset beliefs were intertwined with their beliefs about STEM and general mindset. Students’ views about chemistry mindset were complex and difficult to disentangle from their general mindset views and the views discussed often depended on the context of the interview topic. Finally, with the limited nature of a multiple-case study approach, generalizations cannot be made about the particular views expressed by each individual. They provide some evidence that other mindset views exist but cannot speak to the prevalence of each view in the population. Likewise, data saturation of mindset perspectives cannot be confirmed from a small sample size resulting from this methodological approach. Further studies can shed light on mindset category generalizability through analysis of larger samples. Although student identities were not thoroughly explored based on the data collected in this study, future studies should investigate how student identities affect their domain-specific mindset beliefs.Results and discussion
Case descriptions and mindset perspectives
Six distinct chemistry mindset perspectives were uncovered as themes in the case interviews. These six perspectives were labeled: (1) “Interest,” (2) “Confidence,” (3) “Natural Baseline,” (4) “Some Abilities,” (5) “Most Abilities,” and (6) “All or Nothing Ability.” These are outlined in Table 1. Table 2 presents a more detailed description of each case organized by the three lenses of mindset beliefs, behaviors, and challenges. The mindset perspectives used below were developed as themes for each case with respect to their beliefs about chemistry specifically. This analysis was conducted to address the first research question: How can differences in chemistry mindset be characterized considering students’ beliefs on the nature of chemistry-related abilities, interpretations of challenge, and behavioral responses?| Chemistry mindset perspective | Description | Case |
|---|---|---|
| Interest | • Anyone can improve or develop chemistry intelligence in areas they naturally lack. | Yosef |
| • Interest is a key motivator for the effort required to improve. | ||
| Confidence | • Anyone can improve any aspect of chemistry intelligence, but confidence is a key ingredient to realize that change is possible. | Natalie |
| • Chemistry intelligence develops over time and naturally weak areas can be improved with effort and experience. | Teresa | |
| Natural baseline | • Despite acknowledging that aspects of chemistry intelligence are naturally set at certain levels, they aren’t fixed and can improve to any level with the necessary effort. | Johnny |
| • The effort required for different people to reach the same level will vary depending on natural strengths and weaknesses. | Kevin | |
| Some abilities | • Some abilities are naturally weak and stable or naturally strong and can be improved with effort. | Camille |
| • Belief in the ability to improve is a significant factor in whether or not it is possible. | ||
| Most abilities | • If someone is naturally intelligent in chemistry, they are able to improve to a greater extent than someone who is not naturally intelligent in chemistry. | Raquel |
| • Both types of students are able to apply effort to improve their ability and achieve some level of success. | ||
| All or nothing ability | • Tends to view chemistry intelligence as a single ability that is either naturally present or not. | Elle |
| • Someone who does not have this natural ability can apply effort to get by well enough but they won’t become more intelligent in that area. | ||
| Case | Background | Mindset beliefs | Challenges | Behaviors |
|---|---|---|---|---|
| Yosef | • Biochemistry major | • Interest is a driving force for change. | • Creativity is an ability in chemistry that Yosef feels he lacks naturally and must develop. | • Learns from mistakes rather than avoiding them. |
| • Lifelong interest in science | • Interest and talent can be natural or developed. | • Earned a low grade on an exam in organic chemistry and used the experience to change habits. | • Desires improvement and welcomes feedback. | |
| • Family support for education and high grades | • External influences can spark interest. | • Focuses on small intrinsic rewards and avoids comparison with others. | ||
| • Anything lacking naturally can be developed. | • Seeks help from the instructor. | |||
| • Failure experiences drive improvement. | ||||
| Natalie | • Post-baccalaureate | • Shifted beliefs about improvability of chemistry-specific abilities to include all aspects after seeing significant self-improvement in weak areas. | • Challenges managing time with external pressures. | • Grades have come to signify alignment of understanding with expectations rather than a measure of ability and are used for self-evaluation. |
| • Threatening academic environment discouraged medical pursuit | • Defines intelligence as a willingness to learn from mistakes. | • Remote learning presented challenges during complex theoretical content segments. | • Often seeks help. | |
| • Wrestled with imposter syndrome | • Intelligence develops over time and can be improved by anyone in any area. | • Feelings of challenge are a good indicator that ability needs to improve in an area. | • Mistakes are valuable if they are overcome and produce change. | |
| • Renewed intention to pursue medicine | ||||
| • Fear of chemistry has turned to enjoyment | ||||
| Teresa | • Premedical | • Anything can be improved with effort. | • Perception of challenges has shifted with confidence levels and now leads to increased effort and help seeking. | • Changes to her confidence in chemistry dramatically affected behaviors. |
| • First-STEM major in family | • Chemistry intelligence develops with experience. | • Previously, challenges confirmed beliefs that chemistry intelligence was not natural for her. | • Low grades used to imply low ability, but now motivate effort. | |
| • Initially intimidated by reputation of organic chemistry | • Natural abilities are not genetic but developed early on. | • Previously allowed negative self-perceptions based on comparisons. | ||
| • Grown to enjoy chemistry | • Now regularly seeks help. | |||
| Johnny | • Premedical non-traditional student | • Willingness to put forth effort is key to improving chemistry intelligence. | • It is challenging to read chemistry problems and not know how to begin solving them. | • Understanding is more important than the grade, but the grade measures understanding. |
| • Family history in science and medicine | • Any aspect of chemistry intelligence can be improved. | • Another challenge is not knowing how to check the work done to solve a problem. | • He boosts his confidence in chemistry by developing creative explanations and helping others. | |
| • Always learned quickly | • Accumulation and application of knowledge are the definitions of intelligence he used to explain his own improvement in chemistry. | • New content can be overwhelming, but repeated practice can help problems feel more natural. | • Comparison with others isn’t helpful and mistakes are useful for learning. | |
| • Doesn’t view math and science as his natural abilities | • Has improved his mathematical thinking in chemistry over time. | |||
| • Negative feelings toward chemistry in high school that have become positive in college | ||||
| Kevin | • Neuroscience major | • Different aspects of chemistry intelligence are more (or less) easily changed, but no ability is static. | • Does not experience challenges with grades in chemistry. | • Holds self to high standards and uses grades as a measure of understanding. |
| • Lifelong passion for science | • Individuals begin with various ability levels as a “baseline” but can improve to any level desired with effort. | • Described challenges with distinctions in complex applications of chiral synthesis. | • Would ask for help if challenged, but more often helps others. | |
| • Experiences of success in school from childhood | • The rate of change can vary drastically between people. | • The stress of feeling lost with the content was something he used to motivate seeking understanding. | • Views learning chemistry as a collaborative activity. | |
| • Self-perception as smart | ||||
| • Failure experiences applying to college | ||||
| • Re-evaluated beliefs about the necessity of effort | ||||
| Camille | • Pursuing a career in neurosurgery | • Some abilities in chemistry can be changed more easily than others. | • Has challenges with the language aspect of chemistry (applying correct terminology). | • Grades are important for the evaluation of your abilities by others. |
| • Interest in medicine since childhood | • “Mindset” affects a person's ability to succeed in chemistry, but also the natural abilities that person has. | • Describes having challenges with believing she can improve in certain areas and it fluctuates on different days. | • Tends to procrastinate when her grades are high. | |
| • Gifted education | • If some abilities are naturally weak, they are stable. | • Mistakes allow her to see that she is improving and can be motivating to try harder. | ||
| • Family emphasis on grades | • Some abilities can only be developed. | • Willing to ask for help and enjoys helping others. | ||
| • Previously disliked chemistry | ||||
| • Now enjoys explaining chemistry to others | ||||
| Raquel | • Medical career aspirations | • Makes a distinction between chemistry ability and chemistry intelligence and believes that chemistry ability is more changeable through effort. | • Faced visualization challenges when learning about crystal structures. | • Doesn’t avoid challenging problems and uses them to learn and improve. |
| • Values chemistry and finds it interesting | • Natural abilities can improve if a person has them. | • Overcame this challenge by examining various representations until she was able to visualize better. | • Compares her grades with others to feel better about her own grades. | |
| • Family is supportive of education | • Someone who doesn’t have a natural ability can’t do much to improve it. | • Describes competing with herself in performance expectations. | ||
| • Self-perception as smart | • Values effort over “direct intelligence.” | • Will listen to constructive feedback but tends to avoid negative feedback and mistakes. | ||
| • Tends to earn A grades in chemistry | ||||
| Elle | • Nutrition science major | • Makes a distinction between chemistry ability and chemistry intelligence and believes that chemistry ability is more changeable through effort and memorization. | • Study and learning strategies to be successful in chemistry have been the greatest challenge. | • Uses grades as a primary gauge of success. |
| • Pursuing a career in endocrinology | • Natural abilities are the key determinant in whether someone will be good at chemistry. | • Motivation is also a major challenge. | • Believes she should learn from mistakes and shouldn’t avoid paying attention to feedback. | |
| • Experience with a diabetic parent | • Doesn’t make distinctions between overall chemistry intelligence and aspects of it that could come naturally to different people. | • Describes only doing enough to get by in chemistry, which is not something she does in other classes. | • Has worked with a tutor to improve in chemistry. | |
| • Parents work in and value education | • The tutor and changes to study strategies have helped to overcome some of these challenges. | • Tends to skip challenging problems and doesn’t feel like trying when she can’t solve a problem. | ||
| • Naturally good at math and science (not chemistry) | • Competes with others in her mind, but often this makes her feel inadequate. | |||
| • Does not like chemistry | ||||
Chemistry mindset perspectives as a continuum
Comparing across cases, it is apparent that students discuss very different factors as a rationale for their mindset beliefs in chemistry. Likewise, the implications of their beliefs on their behaviors seem to be dependent on their perceived competence beliefs about themselves as well as their interpretations of challenges. Multiple perspectives were uncovered by considering each case individually. Three students expressed views that fall on the growth end of a mindset continuum (Yosef, Natalie, and Teresa), while two expressed more fixed views (Raquel and Elle). Much of Dweck's work described and treated general mindset as a binary construct (despite measuring it with an ordinal scale): either students have growth mindsets or they have fixed mindsets and the students in the middle of these two cutoffs were simply left out of analyses (Dweck et al., 1995a; 1995b, Dweck and Leggett, 1988). This description of chemistry mindset perspectives contrasts this approach by placing each participant at different locations between full growth and fixed mindset ends of a spectrum.Elle and Yosef expressed views that represent the far ends of the mindset spectrum from one another, Elle being the most fixed in mindset and Yosef holding the strongest growth beliefs. A possible explanation for the strength of Yosef's growth convictions is his high ability in chemistry, which he doesn’t attribute to natural ability, but rather effort and interest. He believes he developed chemistry intelligence through combined effort and interest, so he must think that to be true for anyone. Yosef shares his beliefs about people's ability to improve with the following statement, “I know for a fact, based off of experience that if you put in effort for any small thing – if you really want to put in effort, you can definitely change that. There's like nothing that's impossible to change – unless you're like, not biologically capable of doing it, I think an average person has the ability to change no matter what it is.”
Elle has had the opposite experience of Yosef's. She has low interest and low perceived ability. She thinks the ability portion is natural and thus does not have an interest in chemistry because it's not easy or relatable. She does believe effort makes a difference in her performance, but not as much in her chemistry intelligence, which she doesn’t care as much about regardless. Elle expresses her frustration with learning chemistry in the following:
“Sometimes chemistry will just push me to a point where I just do not want to even try because it just tests me so much and I just don't know what else to do. And challenging problems are…I'm not saying I don't do them at all, but I definitely don't do as many as I should – because I think if I did, I would really be trying to, like, get A's on tests and I really don't do that. I really just try to get a B. And that's just so weird to even say, ‘cause that is really not who I am, to try to get a B, but that's who I am in chemistry…I don't want to be burned when I don't get an A. Because I don't expect it because I know I'm not putting in the effort that is required of an A in chemistry. And just all the different factors – Not naturally being good at it, not really wanting to work at it, wanting to do the bare minimum just to try to get good enough. – It's like an internal issue.”
None of the students’ perspectives completely overlapped, but a few students had sufficient similarities in their views to be categorized within the same mindset theme. First, both Kevin and Johnny seemed convinced that natural abilities are important to how easily you can understand chemistry, but both also expressed that any ability can be developed and equated that to increasing chemistry intelligence. For example, Johnny says,
“I would contrast it as, the natural ability would be like the clay and developed with effort is when you take that clay and mold it into something with edges and, like, corners and, you know, so it becomes something more defined, as opposed to just this big blob of material or matter…I feel like you can have these natural abilities but you still need to do something to shape them and hone them…if you don't, then you could have all the natural ability in the world – It's almost like raw potential. Somebody could have potential, but never meet that potential or meet that promise.”
Second, both Teresa and Natalie expressed confidence that chemistry intelligence is improvable through effort but required performance feedback to create a sense of confidence that growth is possible for themselves as well. To provide evidence for this belief, Natalie shared her experience with improving in visualization skills in organic chemistry: “The ability to rotate models in my head, I've gotten a lot better at that…It feels like a silly small thing, but it's been really rewarding…I used to not be able to do this at all or understand what it is. And now I feel like I kind of know what's happening.”
Another similarity was the strength of emphasis placed on natural abilities by both Camille and Raquel. Nevertheless, they were able to believe themselves capable of growth in chemistry due to their own natural abilities for STEM subjects. Despite this similarity, Camille expressed that increasing chemistry intelligence is possible through effort focused on developing relevant abilities, though she felt that not all abilities were changeable. Her belief about stable abilities is apparent in the following quote: “I think it's just differences that we all have as humans. There are people that are always going to be able to have a better memory than most. Like, I have a terrible memory.
No matter what I do I can't necessarily change it. The only thing I can do is improve on how I try to relate the information that I'm trying to remember to things that I've – Things that I know, things that just come naturally to me.” Raquel expressed a higher number of abilities as difficult to change, showing a belief that natural abilities are mostly stable.
Based on the similarities and differences discussed above, different categories of mindset perspectives can be defined. These mindset perspectives are presented in Fig. 3 along a hypothetical continuum. Elle carries the traditional implications of fixed beliefs, in that, if a person doesn’t have natural ability for chemistry, then there is not much that can be done other than to protect one's ego through avoidant behaviors. Raquel is a bit more open to the idea of improving chemistry intelligence but much more for those who have the natural ability to begin with. Camille has a slightly more flexible view on overall chemistry intelligence but emphasized specific abilities as unchangeable and the need to leverage the natural abilities you do have to improve. Kevin and Johnny both believe that development of any ability is possible, yet state that natural ability plays a role in how easily one can learn. They were placed at the same point on the continuum in Fig. 3 because their views are similar, yet Kevin has more confidence because he views himself as having natural ability for chemistry and Johnny does not. Johnny instead has a natural interest (or curiosity) and thus is willing to develop his weak areas. Teresa and Natalie both believe that anything can be developed yet were hesitant to believe this about their own chemistry intelligence without evidence supporting that they could improve. Teresa's shift from lack of confidence in general chemistry to complete enjoyment of the success she found in organic chemistry is more substantial than the changes Natalie experienced. This could suggest that Natalie's mindset beliefs are more deeply ingrained and drive her effort to improve, while Teresa has exerted effort out of a desire to succeed and her mindset beliefs followed her improvement. Finally, Yosef expressed very optimistic views regarding anyone developing abilities if they have interest in that domain. He did acknowledge that some people have a “God-given talent” for certain subjects, but also said that everyone must work hard to be good at chemistry. His main comparison between students who do well in chemistry and those who do not was based on the amount of effort they apply as driven by their personal interests. He also stated that educators play a significant role in how personally interesting a course is through their own enthusiasm for the content.
 | ||
| Fig. 3 Case participants’ mindset perspectives organized along a continuum from most fixed to most growth mindset. The colors along the continuum represent the degree of growth or fixedness of a given mindset perspective theme. | ||
Chemistry mindset perspectives in two-dimensions
Another way to consider the contrasts in students’ views discussed in the interviews is to consider the dimension of chemistry mindset beliefs about others as a separate continuum from beliefs about self. Studies investigating physics-specific beliefs have described a similar phenomenon (Kalender et al., 2022; Malespina et al., 2022). Students’ beliefs about their own intelligence may relate to their beliefs about others; however, it is entirely possible that their views about self may be substantially more or less optimistic than their views about other people. Fig. 4 presents a two-dimensional translation from the single-dimension continuum in Fig. 3 as an alternative theoretical placement of each case based on their individual revealed perspectives. The top right quadrant represents a growth mindset regarding both self and others, resembling the traditional view of a growth mindset most closely. For this reason, Yosef belongs in this quadrant as he was positive that anyone can grow, including himself. Kevin and Johnny might also be placed in this region slightly closer to the axes as they both expressed optimistic views about self and others. The bottom right considers others more capable of growth than the self, characteristic of the two students who tended to doubt themselves in the absence of positive performance feedback, yet firmly believed in development over time (Natalie and Teresa). The top left quadrant represents the view that growth is more possible for “me” relative to others, a possibility when a student holds high self-efficacy beliefs and views their own relevant abilities as naturally occurring. Two students seem to fit well in this region, namely, Camille and Raquel, with Camille slightly more optimistic about both others and self. Finally, the bottom left quadrant is the most analogous to the traditional view of a fixed mindset. Beliefs that natural ability in a specific area dictates one's intelligence for that subject. Elle most clearly expressed this view toward chemistry for herself and others and has thus been placed in the corresponding region of the plot. | ||
| Fig. 4 Qualitative placement of each case along two dimensions of mindset: myself and others. The same colors for each case are used from Fig. 3 to indicate the degree of growth or fixedness of each mindset perspective uncovered when considering a single mindset dimension. | ||
Results from the survey measures indicate that the case study participants tended to have more fixed mindsets about others’ chemistry intelligence compared to their own. Aligning with the interview findings, Yosef was shown to have the highest growth mindset about his own chemistry intelligence as well as that of other students. Raquel and Elle both scored the lowest on their chemistry mindset beliefs as well as their beliefs about others, with Raquel reporting the most fixed chemistry mindset about others. Johnny and Teresa both reported a more growth mindset about others compared to themselves, aligning with the expressions of self-doubt and lack of natural ability in the area of chemistry described in their interviews. All participants reported a chemistry mindset on the growth end of the scale, but the degree of growth chemistry mindset about self and others aligned well with the interview findings.
The other important result from the survey measures included in the case study was the relationship between chemistry mindset about self and the participants’ self-report of mindset-related behaviors. Behaviors were consistently slightly less growth mindset compared to the measured chemistry mindset belief. This meant that the students with the highest reported mindset-related behaviors were those who also held the most growth beliefs about their own chemistry intelligence. Combined with the observation that mindset beliefs about self and others do not always align, this finding suggests that chemistry mindset beliefs about the self most strongly predict a student's mindset-related behaviors (i.e. avoidance, persistence, reception of critique, etc.). It should be noted that only qualitative observations can be made about these survey results with a sample size of 8 participants and generalizations cannot be made based on this evidence alone.
To address the second research question, rather than considering three categories of mindset (growth, middle, and fixed) as the traditional mindset literature suggests (Dweck et al., 1995a; Hong et al., 1999), we can consider the four quadrants of the two-dimensional conception of mindset (Kalender et al., 2022; Malespina et al., 2022). The upper right and bottom left quadrant represent the growth and fixed labels as previously defined; however, with a richer measurement distinction as a combination of two dimensions. In contrast, the bottom right and upper left quadrants may shed additional light on the messy middle described in the mindset literature (Hong et al., 1999).
Alignment of chemistry mindset with general mindset
In considering the distinctiveness of chemistry mindset as a construct, these cases can be compared to theoretical mindset perspectives in a general context. This analysis addresses the second research question: What degree of alignment is observed between interview themes and extant general intelligence mindset theory to provide insight into chemistry mindset as a distinct construct?It must be acknowledged that the academic environment in which all students in this study were enrolled places a general emphasis on performance above mastery and the primary tool provided for diagnosing one's mastery is performance scores. If academic environments aim to promote mastery and growth beliefs, the performance focus may need to be reconsidered due to its impact on students’ interpretations of their own success.
Based on the criteria described for a growth mindset, Yosef, Kevin, Johnny, and Natalie would all be considered to have a growth mindset. In the case analyses described here, Yosef, Teresa, and Natalie were all considered to have chemistry mindsets aligning with growth beliefs and Johnny and Kevin were considered to lie on the growth end of the spectrum. Some caveats to this classification process were misalignment of beliefs about oneself relative to others and some emphasis on natural abilities or an innate interest in chemistry. Thus, a growth chemistry mindset appears to be more complex in nature than a theoretical general growth mindset.
Raquel meets two of these fixed mindset criteria. She explains the differences between people's achievement in chemistry by way of their natural abilities. She also admits to using some degree of competitive behavior in comparing her grades to others to boost her self-esteem. However, she does not give up in the face of challenges and rather becomes more motivated when challenges arise because she believes herself capable of overcoming them due to her high natural ability. Camille meets the first criterion (natural abilities) to some degree, but is a bit more flexible in that view, and does not meet any of the others. Teresa met the third criterion to some degree in her first interview (performance and competition) but attempts to minimize these comparisons to maintain her confidence and does not meet the others. Classifying a chemistry fixed mindset, as indicated by these cases, is complex. Some of the criteria from a theoretical fixed general mindset aligned with students whose views approached a growth chemistry mindset. This is due to misalignment of beliefs about the self and others in chemistry because of perceived self-competence.
Conclusion
Within-case analysis using the mindset meaning model revealed some nuances in chemistry mindset that have not been previously described for general mindset. First, it appears that multiple types of growth mindset may exist, some more deeply ingrained than others and regarding oneself as well as others. Likewise, some variation in middle mindset beliefs was observed in that two students were closer to having growth mindset beliefs while the third student had more mixed beliefs depending on which abilities were natural and stable and which were changeable with effort. Finally, two kinds of fixed mindset categories were uncovered as a function of ability perceptions. One student viewed herself as having high chemistry ability and experienced less challenge and therefore exhibited a very different profile from the student with low chemistry ability perceptions and a high degree of challenge. This analysis resulted in the ability to expand the traditional mindset continuum to include each perspective sequentially. These findings suggest that inclusion of challenge interpretations and behavior in quantitative measures is important to more accurately identify an individual's chemistry mindset perspective (RQ1).Furthermore, evidence for multiple dimensions of chemistry mindset beliefs was uncovered in this case study as a function of who is being considered (self versus others). Similar dimensions have been uncovered for undergraduate students’ physics mindset beliefs (Kalender et al., 2022; Malespina et al., 2022). Moreover, the degree of ingrained beliefs about oneself was found to correspond to interpretations of challenge and behavioral responses to challenge. This suggests that although there is substantial alignment of chemistry mindset, behaviors, and challenges observed in these case participants with extant literature findings, the object of mindset items (me or someone else) are critical to predictive measures of an individual's behaviors (RQ2).
Implications for research and teaching
Mindset has long been conceptualized as a meaning system that individuals operate out of (Hong et al., 1999). It has also previously been reported that the referent (me versus others) impacts the predictive relation between mindset measures and outcomes (De Castella and Byrne, 2015). Although these layers have been considered previously in the measurement of mindset, little attention has been given to the possibility that they can be combined to provide a richer description of students’ mindset views for categorical comparisons. A comprehensive measure addressing all these aspects has not been developed for undergraduate students and particularly not for domain-specific contexts. This case study revealed that chemistry mindset does not align perfectly with general mindset models, suggesting these to be separate constructs. Likewise, mindset may have unique characteristics and perspectives within other science disciplines.The multiple aspects involved in a rich description of a students’ chemistry mindset could be considered for better triangulation of their true beliefs and the depth to which they hold such beliefs. The more accurate our description of a student's mindset, the more appropriate predictions could be made regarding their success in chemistry courses. One method to resolve this concern for large-scale mindset classification would be to create a multidimensional mindset instrument, specific to chemistry, that addresses each construct as a subfactor: (1) chemistry mindset about self, (2) chemistry mindset about everyone, (3) interpretation of challenge in chemistry, and (4) behavioral response to challenge in chemistry. These additional factors can allow for the creation of many mindset categories that could each be evaluated for their relative contribution to the predictive relationship of mindset with student success outcomes.
In chemistry classrooms, a variety of student affective profiles are present. Feedback carries different meaning to each student as a function of their mindset beliefs and self-perceptions of chemistry ability. Chemistry instructors should be aware of this when providing feedback to students and emphasize the ways in which it is beneficial to their improvement rather than evaluative of their ability. Also, students may place different degrees of emphasis on the effects of effort toward impacting their intelligence or just their “ability.” Noticeable improvements in mastery or skills can be emphasized by instructors over simple grade improvements or “native abilities.” Because students were more likely to exhibit mastery behaviors when they had high ability self-perceptions, instructors can impact students’ mindset beliefs about the self by placing emphasis on improvements over the scores themselves, which is likely to increase ability perceptions and thus foster growth beliefs about the self.
Another avenue for impacting students was suggested by one of the case participants, Yosef. He perceived his instructors as impacting his interest through their enthusiasm and passion for the subject of chemistry. It is important for instructors to remember that they serve as role models for students. Instructors can share how they improved their own intelligence in chemistry through effort and that they believe this to be possible for any student who is willing to engage in it. This can be specifically targeted as individual skills, such as visualization, mathematical thinking, and disciplinary language usage. Instructors can also share topics of personal interest related to course content and provide opportunities for students to see how the concepts being covered are relevant to them.
Data availability
The data supporting this article have been included as part of the Appendix and ESI.†Conflicts of interest
There are no conflicts to declare.Appendices
Appendix A. Initial in-depth semi-structured interview protocol with tasks developed based on general mindset literature
Fall 2020 Semi-Structured Interview ProtocolDescription of phases and questions students will be asked to respond to using think aloud:
Phase 1: Beginning questions to practice talking:
• How is the current semester of chemistry going?
• What course are you in, how are your grades, do you feel challenged currently in chemistry?
• What do you notice about someone in chemistry class that makes you think they are smart/intelligent?
• Do you recall family members praising you more for your ability or for your effort in school?
• Do you think your chemistry intelligence is the main factor determining your chemistry performance?
• Why or why not? (How would you define it?)
• Can you tell me about a time when you faced a challenge in chemistry? What happened, how did you respond, and what was the end result?
Phase 2: Behaviors in challenging chemistry scenarios selection activity (Appendix A Fig. 5 and 6)
 | ||
| Fig. 5 Entity behavior item task. | ||
 | ||
| Fig. 6 Incremental behavior item task. | ||
• Which of these items can you see yourself doing this semester when you experience challenges in chemistry? Circle the ones you think are relevant to you and cross out those that you don’t think you would do.
• For some of the circled responses: Can you give an example of a time you did that in a class?
• For some of the crossed out responses: Why did you cross that out (social desirability?)? Why don’t you think you would do that?
Phase 3a: Beliefs about cognitive abilities important to chemistry intelligence selection activity (Appendix A Fig. 7)
 | ||
| Fig. 7 Chemistry intelligence aspects task. | ||
• Which of these items are the most important aspects of chemistry intelligence? Circle the ones you agree with and cross out those you don’t agree with.
• Are there any other aspects you would like to add to this list?
• Which of these are aspects you feel like you are good at vs not good at? Why?
Phase 3b: Beliefs about chemistry intelligence cognitive abilities origins sorting activity (Appendix A Fig. 8)
 | ||
| Fig. 8 Natural and developed chemistry intelligence aspects sorting task. | ||
• Out of the items you circled, how do you think you get those abilities? Are they developed or natural abilities?
• What do you think “natural ability” means?
• Why do you think these are developed? Can you give an example?
• Why do you think these are natural abilities? Can you give an example?
Phase 3c: Beliefs about chemistry intelligence cognitive abilities malleability within origin sorting activity (Appendix A Fig. 9)
 | ||
| Fig. 9 Malleability of chemistry intelligence aspects sorting task. | ||
• Can you sort each of these into those that you can change versus those that you cannot change?
• How would you define “change” in this case?
• What evidence of change would you look at to verify that it had happened?
• Why do you think these can change?
• Why do you think these cannot change?
Phase 4: Discussion of survey response reasons and discrepancies with currently stated beliefs
• Here are the responses you selected from the survey earlier this semester.
• Why did you choose this answer before? What were you thinking when you read the question? (social desirability?)
• Today, you said this behavior/answer, but/and on the survey you said this. Why do you think they were different/the same?
Phase 5: Graphing of intelligence over lifetime activity (Appendix A Fig. 10)
 | ||
| Fig. 10 Intelligence level over lifetime graphing task. | ||
• How do you define intelligence as a whole?
• What do you think a graph of intelligence vs. time looks from birth, through childhood, adolescence, adulthood, and elderly stages until death for the average person?
• How should the graph look for the average chemist?
• Can you explain why you drew each graph the way you did?
• Why is the graph for the average person the same/different from the average chemist?
• Can you compare the max intelligence you drew and the shape of each graph?
Phase 6: Final questions
• Do you think that people can change their intelligence in chemistry? How did you come to believe this?
• Throughout your chemistry courses, has your confidence in your chemistry ability changed? How and why?
• Have you ever dropped, withdrawn, or failed a chemistry course? If so, what factors influenced that decision/event?
• Do you often doubt your ability to succeed in chemistry? If so, what causes you to think that way?
• Have you ever said (or believed) that you aren’t good at chemistry? Why?
Appendix B. Follow-up semi-structured interview protocol.
Fall 2021 Follow-up Interview Protocol: Multiple Case StudyDescription of phases and questions students will be asked to respond to using think aloud:
Phase 1: Beginning questions to practice talking and reflecting on past experiences and what brought them to this point.
• What class are you currently in and what is your major/reason for taking chemistry?
• What led you to select your major? What are your career goals?
• Can you tell me a bit about your background? What were some influences on your academic/career goals?
• What were some influences on what you value as demonstrating intelligence?
• Can you discuss your experiences with chemistry before college? What is your history with chemistry?
Phase 2: How does the student view their identity with regards to science and/or chemistry?
• How well do you feel that you fit in as a science major? What about in a chemistry class? Why do you see yourself that way?
• What kind of person do you think becomes a chemist?
Phase 3: What are external factors affecting the student's beliefs about chemistry?
• How do your family and friends talk about chemistry and/or your major? Do they seem to think it requires natural ability or very smart people?
• How do you think your chemistry instructors view your intelligence in chemistry? Do they seem to think it can change?
• How does society/our culture/everyday person view chemistry in terms of difficulty/ability?
• Do you agree with these different perspectives about chemistry? Why or why not?
Phase 4: What are the student's internal beliefs about chemistry and challenge experiences?
• How challenging do you believe chemistry is? Is it more or less challenging to you compared to your peers? Why do you think this is? Were there differences between organic and general chemistry in terms of difficulty?
• What is the most challenging aspect of chemistry to you?
• What are some specific challenges you have faced in chemistry classes? How big of a challenge was it? When did it happen and how did you feel? What did you do?
• What does encountering a challenge in chemistry mean to you (low ability or more effort)? How does that make you feel? What do you do when there's a challenge?
Phase 5: What are some behaviors the student acknowledges being important to their success?
• What is something you achieved in chemistry that you are very proud of?
• What is something you did in chemistry that you are not so proud of?
Phase 6: Previous interview activity results
• Show either the categorization of chemistry abilities, the natural ability vs developed abilities, or the plot for intelligence and ask further questions to clarify perspective and gauge changes in beliefs.
Phase 7: Final questions (What is the student's mindset toward chemistry and has it changed?)
• Is your ability to do chemistry something that you could improve in? How would that happen? What are some aspects that could be improved?
• Has the way you feel about your ability to do chemistry changed over time? How and why?
• Do your feelings about your ability to do chemistry change in certain scenarios? Can you give examples?
• Is chemistry something that you could see a career in? Why or why not?
Acknowledgements
This material is based upon work supported by the National Science Foundation under Grant No. 2111182. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We would also like to thank Alexis Bullock for their contribution to the data analysis for this manuscript.References
- Aronson J., Fried C. B. and Good C., (2002), Reducing the Effects of Stereotype Threat on African American College Students by Shaping Theories of Intelligence, J. Exp. Soc. Psych., 38(2), 113–125 DOI:10.1006/jesp.2001.1491.
- Bandura A., (1986), Social foundations of thought and action: A social cognitive theory, Englewood Cliffs, NJ, US: Prentice-Hall, Inc.
- Bandura M. and Dweck C., (1985), The relationship of conceptions of intelligence and achievement goals to achievement – Related cognition, affect and behavior, Unpublished manuscript.
- Barger M. M., (2019), Connections Between Instructor Messages and Undergraduate Students' Changing Personal Theories About Education, J. Exp. Educ., 87(2), 314–331 DOI:10.1080/00220973.2018.1469111.
- Burkley M., Parker J., Paul Stermer S. and Burkley E., (2010), Trait beliefs that make women vulnerable to math disengagement, Personality Indiv. Diff., 48(2), 234–238 DOI:10.1016/j.paid.2009.09.002.
- Burnette J. L., O'Boyle E. H., VanEpps E. M., Pollack J. M. and Finkel E. J., (2013), Mind-sets matter: a meta-analytic review of implicit theories and self-regulation, Psych. Bull., 139(3), 655–701 DOI:10.1037/a0029531.
- Canning E. A., Muenks K., Green D. J. and Murphy M. C., (2019), STEM faculty who believe ability is fixed have larger racial achievement gaps and inspire less student motivation in their classes, Sci. Adv., 5(2), eaau4734 DOI:10.1126/sciadv.aau4734.
- Carver C. S. and Scheier M. F., (2001), On the Self-Regulation of Behavior, Cambridge University Press.
- Costa A. and Faria L., (2018), Implicit Theories of Intelligence and Academic Achievement: A Meta-Analytic Review, Front. Psych., 9, 829 DOI:10.3389/fpsyg.2018.00829.
- Dai T. and Cromley J. G., (2014), Changes in implicit theories of ability in biology and dropout from STEM majors: a latent growth curve approach, Contemp. Educ. Psych., 39(3), 233–247 DOI:10.1016/j.cedpsych.2014.06.003.
- De Castella K. and Byrne D., (2015), My intelligence may be more malleable than yours: the revised implicit theories of intelligence (self-theory) scale is a better predictor of achievement, motivation, and student disengagement, Eur. J. Psych. Educ., 30(3), 245–267 DOI:10.1007/s10212-015-0244-y.
- Diener C. I. and Dweck C. S., (1978), An analysis of learned helplessness: continuous changes in performance, strategy, and achievement cognitions following failure, J. Personality Soc. Psych., 36(5), 451.
- Doron J., Stephan Y., Boiché J. and Scanff C. L., (2009), Coping with examinations: exploring relationships between students' coping strategies, implicit theories of ability, and perceived control, British J. Educ. Psych., 79(3), 515–528.
- Dweck C. S., Chiu C.-Y. and Hong Y.-Y., (1995a) Implicit theories and their role in judgments and reactions: a word from two perspectives, Psych. Inquiry., 6(4), 267–285 DOI:10.1207/s15327965pli0604_1.
- Dweck C. S., Chiu C.-Y. and Hong Y.-Y., (1995b), Implicit theories: elaboration and extension of the model, Psych. Inquiry, 6(4), 322–333.
- Dweck C. S. and Leggett E. L., (1988), A social-cognitive approach to motivation and personality, Psych. Rev., 95(2), 256.
- Good C., Aronson J. and Inzlicht M., (2003), Improving adolescents' standardized test performance: an intervention to reduce the effects of stereotype threat, J. App. Dev. Psych., 24(6), 645–662 DOI:10.1016/j.appdev.2003.09.002.
- Good C., Rattan A. and Dweck C. S., (2012), Why do women opt out? Sense of belonging and women's representation in mathematics, J. Personality Soc. Psych., 102(4), 700–717 DOI:10.1037/a0026659.
- Gorson J. and O'Rourke E., (2019), How Do Students Talk About Intelligence? An Investigation of Motivation, Self-efficacy, and Mindsets in Computer Science, Paper presented at the Proceedings of the 2019 ACM Conference on International Computing Education Research, Toronto ON, Canada DOI:10.1145/3291279.3339413.
- Guest G., Namey E. and Chen M., (2020), A simple method to assess and report thematic saturation in qualitative research, PLoS One, 15(5), e0232076.
- Gunderson E. A., Hamdan N., Sorhagen N. S. and D'Esterre A. P., (2017), Who needs innate ability to succeed in math and literacy? Academic-domain-specific theories of intelligence about peers versus adults, Dev. Psych., 53(6), 1188.
- Harris R. B., Mack M. R., Bryant J., Theobald E. J. and Freeman S., (2020), Reducing achievement gaps in undergraduate general chemistry could lift underrepresented students into a “hyperpersistent zone”, Sci. Adv., 6(24), eaaz5687 DOI:10.1126/sciadv.aaz5687.
- Hong Y.-Y., Chiu C.-Y., Dweck C. S., Lin D. M.-S. and Wan W., (1999), Implicit theories, attributions, and coping: a meaning system approach, J. Personality Soc. Psych., 77(3), 588.
- Horowitz G., Rabin L. A. and Brodale D. L., (2013), Improving Student Performance in Organic Chemistry: Help Seeking Behaviors and Prior Chemistry Aptitude. J. Scholarship Teach. Learn., 13(3), 120–133.
- Howell A. J. and Buro K., (2009), Implicit beliefs, achievement goals, and procrastination: a mediational analysis, Learn. Indiv. Diff., 19(1), 151–154.
- Ibourk A., Hughes R. and Mathis C., (2022), “It is what it is”: using Storied-Identity and intersectionality lenses to understand the trajectory of a young Black woman's science and math identities, J. Res. Sci. Teach., 59(7), 1099–1133.
- Kalender Z. Y., Marshman E., Schunn C. D., Nokes-Malach T. J. and Singh C., (2022), Framework for unpacking students’ mindsets in physics by gender, Phys. Rev. Phys. Educ. Res., 18(1), 010116.
- Karlen Y., Suter F., Hirt C. and Maag Merki K., (2019), The role of implicit theories in students' grit, achievement goals, intrinsic and extrinsic motivation, and achievement in the context of a long-term challenging task, Learn. Indiv. Diff., 74, 101757 DOI:10.1016/j.lindif.2019.101757.
- Koch A. K., (2017), It's About the Gateway Courses: Defining and Contextualizing the Issue, New Dir. Higher Educ., 2017(180), 11–17 DOI:10.1002/he.20257.
- LaCosse J., Murphy M. C., Garcia J. A. and Zirkel S., (2021), The role of STEM professors’ mindset beliefs on students’ anticipated psychological experiences and course interest, J. Educ. Psych., 113(5), 949 DOI:10.1037/edu0000620.supp (Supplemental).
- Leslie S.-J., Cimpian A., Meyer M. and Freeland E., (2015), Expectations of brilliance underlie gender distributions across academic disciplines, Science, 347(6219), 262 DOI:10.1126/science.1261375.
- Limeri L. B., Carter N. T., Choe J., Harper H. G., Martin H. R., Benton A. and Dolan E. L., (2020a), Growing a growth mindset: characterizing how and why undergraduate students’ mindsets change, Int. J. STEM Educ., 7(1), 35 DOI:10.1186/s40594-020-00227-2.
- Limeri L. B., Choe J., Harper H. G., Martin H. R., Benton A. and Dolan E. L., (2020b), Knowledge or Abilities? How Undergraduates Define Intelligence, CBE—Life Sci. Educ., 19(1), ar5.
- Little A. J., Humphrey B., Green A., Nair A. and Sawtelle V., (2019), Exploring mindset's applicability to students' experiences with challenge in transformed college physics courses, Phys. Rev. Phys. Educ. Res., 15(1), 010127 DOI:10.1103/PhysRevPhysEducRes.15.010127.
- Little A., Sawtelle V. and Humphrey B., (2016), Mindset in context: Developing new methodologies to study mindset in interview data, Paper presented at the Physics Education Research Conference Proceedings.
- Lou N. M. and Noels K. A., (2016), Changing language mindsets: implications for goal orientations and responses to failure in and outside the second language classroom. Contemp. Educ. Psych., 46, 22–33 DOI:10.1016/j.cedpsych.2016.03.004.
- Lüftenegger M. and Chen J. A., (2017) Conceptual issues and assessment of implicit theories, Zeitschrift für Psychologie, 225(2), 99–106 DOI:10.1027/2151-2604/a000286.
- Lytle A. and Shin J. E., (2020), Incremental Beliefs, STEM Efficacy and STEM Interest Among First-Year Undergraduate Students. J. Sci. Educ. Tech., 1–10.
- Macakova V. and Wood C., (2020), The relationship between academic achievement, self-efficacy, implicit theories and basic psychological needs satisfaction among university students, Stud. High. Educ., 1–11 DOI:10.1080/03075079.2020.1739017.
- Malespina A., Schunn C. D. and Singh C., (2022), Whose ability and growth matter? Gender, mindset and performance in physics, Int. J. STEM Educ., 9(1), 1–16.
- McKinney L., Novak H., Hagedorn L. S. and Luna-Torres M., (2019), Giving up on a course: an analysis of course dropping behaviors among community college students, Res. High. Educ., 60(2), 184–202.
- Molden D. C. and Dweck C. S., (2006), Finding “meaning” in psychology: a lay theories approach to self-regulation, social perception, and social development, Am. Psych., 61(3), 192.
- Morris C., Ramaswami A., Kramer A. and Dringenberg E., (2019), A Preliminary Study of How Undergraduate Engineering Students Describe Intelligence and Smartness. Paper presented at the 2019 IEEE Frontiers in Education Conference (FIE).
- Muenks K., Canning E. A., LaCosse J., Green D. J., Zirkel S., Garcia J. A. and Murphy M. C., (2020), Does my professor think my ability can change? Students’ perceptions of their STEM professors’ mindset beliefs predict their psychological vulnerability, engagement, and performance in class, J. Exp. Psych.: General, 149(11), 2119–2144 DOI:10.1037/xge0000763.supp (Supplemental).
- Popejoy K. and Asala K. S., (2013), A Team Approach to Successful Learning: Peer Learning Coaches in Chemistry, J. College Sci. Teaching, 42(3), 18.
- Santos D. L., Barbera J. and Mooring S. R., (2022), Development of the Chemistry Mindset Instrument (CheMI) for use with introductory undergraduate chemistry students, Chem. Educ. Res. Pract., 23(3), 742–757.
- Scott M. J. and Ghinea G., (2014), On the Domain-Specificity of Mindsets: The Relationship Between Aptitude Beliefs and Programming Practice, IEEE Trans. Educ., 57(3), 169–174 DOI:10.1109/TE.2013.2288700.
- Shively R. L. and Ryan C. S., (2013), Longitudinal changes in college math students’ implicit theories of intelligence, Soc. Psych. Educ., 16(2), 241–256.
- Sisk V. F., Burgoyne A. P., Sun J., Butler J. L. and Macnamara B. N., (2018), To What Extent and Under Which Circumstances Are Growth Mind-Sets Important to Academic Achievement? Two Meta-Analyses, Psych. Sci., 29(4), 549–571 DOI:10.1177/0956797617739704.
- Stake R. E., (1995), The art of case study research, sage.
- Tai R. H., Sadler P. M. and Loehr J. F., (2005), Factors influencing success in introductory college chemistry, J. Res. Sci. Teach., 42(9), 987–1012 DOI:10.1002/tea.20082.
- van Aalderen-Smeets S. I., Walma van der Molen J. H. and Xenidou-Dervou I., (2019), Implicit STEM ability beliefs predict secondary school students' STEM self-efficacy beliefs and their intention to opt for a STEM field career, J. Res. Sci. Teach., 56(4), 465–485.
- Yin R. K., (2003), Design and methods, Case Study Res., 3(9.2).
Footnote |
| † Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4rp00068d |
| This journal is © The Royal Society of Chemistry 2024 |