Is general chemistry too costly? How different groups of students perceive the task effort and emotional costs of taking a chemistry course and the relationship to achievement and retention
Cassidy L.
Wilkes
,
Madelyn M.
Gamble
and
Guizella A.
Rocabado
*
Department of Chemistry and Physics, Southern Utah University, Cedar City, UT, USA. E-mail: guizellarocabado@suu.edu
First published on 8th June 2024
Abstract
Chemistry is often daunting for college students, contributing to high attrition rates in STEM majors. This study explored students' perceptions of the challenges in studying chemistry, including task effort and emotional costs. We examined how these perceptions, along with goal approaches, impact academic performance and retention in general chemistry. Utilizing cluster analysis of survey data and content analysis from student interviews, we investigated students’ profiles of perceived cost and goal approaches and how these related to the course performance and retention. Our analysis revealed that students who experienced lower perceived costs and were able to focus more on their mastery goals, tend to perform better, and persist in the course at higher rates. Conversely, students who perceived higher costs tend to drop the course more frequently, viewing chemistry as irrelevant to their future goals. These students prioritized performance goals over mastery, resulting in poorer performance. These results suggest that by addressing students’ perceived costs through interventions, students may focus more on their mastery goals, consequently improving their learning and understanding of the material.
Introduction
Chemistry is one of the most feared subjects in college; many students already have a preconceived notion that chemistry is “too difficult” (Gafoor and Vevaremmal, 2013). Despite this view that chemistry is “too difficult”, introductory chemistry courses, such as general chemistry and organic chemistry, are required for students who plan to go into many healthcare professions and science careers (Barr et al., 2008; Cooper et al., 2010). The fear of chemistry, paired with students’ knowledge of its requirement for their future career goals, leads to chemistry having some of the highest attrition and failure rates within the undergraduate curriculum (Seymour and Hewitt, 1997; Gasiewski et al., 2012; Stone et al., 2018; Seymour and Hunter, 2019). General chemistry is often undergraduate students’ first exposure to chemistry; therefore, their perception of this course can change their view on the subject of chemistry as a whole and impact whether they choose to progress with a STEM major (Shultz et al., 2015). These perceptions and outcomes create an intricate and extensive issue that has led researchers to try to identify barriers that may be inhibiting students’ success in general chemistry (Lewis, 2018; Guo et al., 2022; Lee et al., 2022).Barriers in any classroom, but particularly in chemistry, can be cognitive in nature (i.e., Zoller, 1990; Cracolice and Busby, 2015), or affective (i.e., Chan and Bauer, 2016: Fink et al., 2020; Lee et al., 2022). Affective learning describes students’ emotional experiences (affect) and how those emotions influence their learning (Pratt and Raker, 2020). Taber (2015) indicates that taking a joint cognitive and affective approach to teaching and learning is important. Highlighting both the cognitive and affective experiences of students can lead to a more holistic and meaningful approach to helping students learn in high-stakes, demanding courses, such as chemistry. A recent review examined studies in which the affective domain of learning was investigated, concluding that considering and intervening within this domain is of great importance to positively influence academic outcomes in chemistry (Flaherty, 2020).
The affective domain of learning encompasses various constructs among which is motivation. There are various theories of motivation which focus on student expectancies, their reasons for engagement, the integration of motivation and cognition, and the integration of expectancy and value constructs (Eccles and Wigfield, 2002; Gopalan et al., 2017). Theories of motivation that focus on student expectancies, such as the Social Cognitive Theory (Bandura, 1991) and the Expectancy-Value Theory (Eccles and Wigfield, 2020), investigate students’ response to the question, “Can I do this?” (Eccles et al., 1983; Wigfield and Eccles, 2000; Eccles and Wigfield, 2002; Perez et al., 2019). Others such as the Self-Determination Theory (Ryan and Deci, 2000) and the Achievement Goal Theory (Elliot and McGregor, 2001), are motivational theories that focus on students’ reasons for engaging in an activity. Integrative motivational theories look at the relationships or explanations of academic outcomes and motivational achievement attributions, such as the Attribution Theory (Weiner, 1972).
Motivation is a construct that can be described as the driving force propelling individuals towards their desired goals (Gopalan et al., 2017; Singh, 2011). How a student is motivated influences what goals they have for themselves in the course or for their future (Latham and Locke, 1991; Gopalan et al., 2017). Goals provide a sense of direction and purpose, giving the student something they can strive for (Latham and Locke, 1991; Locke and Latham, 2002; Friedman and Mandel, 2009; Gopalan et al., 2017). However, it is important that these goals are attainable, as setting unrealistic goals can lead to feelings of discouragement and demotivation (Locke and Latham, 2002; Pintrich, 2003; Urdan and Turner, 2005). Additionally, barriers such as personal limitations and perceptions or perceived costs can greatly impact motivation in a negative way (Barron and Hullerman, 2015). Overcoming these barriers requires a sense of resilience and determination, and the ability to adjust one's goals or approach in order to achieve success (Martin, 2002). Ultimately, the balance between barriers that threaten motivation, and the goals that can potentially mediate those barriers, is how students navigate their academic journey. Understanding how these valences of motivation relate to each other can help shed light on how to help students navigate challenges and achieve success (Gong et al., 2023).
Expectancy-value-cost theory of motivation
The expectancy-value theory (EVT) was first proposed by psychologist Martin Fishbein in the late 1960s (Fishbein and Ajzen, 1975) and has since undergone several revisions and refinements. Fishbein developed the theory based on his research into attitudes and how they are formed, changed, and are related to behavior. He proposed that individuals' attitudes towards a particular behavior are determined by their beliefs about the outcomes of that behavior (expectancies) and the value they place on those outcomes (Fishbein and Ajzen, 1975). The original Expectancy-Value Theory was then interpreted and used for education research (Eccles et al., 1983). In an academic context, expectancy was explained as students’ perceived judgements of their ability to succeed, and task value was how students perceived level of task importance (Flake et al., 2015). More recently, cost was introduced as an integral part of the framework (Flake et al., 2015; Ko and Marx, 2019; Lee et al., 2022). This elaboration led to the development of the Expectancy-Value-Cost Theory (EVCT).Perceived Cost (PC) refers to what an individual is investing, what is required, or what is given up to engage in a task (Flake et al., 2015; Gong et al., 2023). PC has shown to have a mediative effect on a student's perception of task value, or in other words, how a student perceives the level of cost of a course will relate to the value they place on it (Lee et al., 2022). The three components of EVCT (expectancy, value, and cost) allow a student to answer the following three questions about the course or task: expectancy, “Can I do this?” (Eccles et al., 1983; Wigfield and Eccles, 2000; Perez et al., 2019); value, “Why do I want to do this?” (Eccles et al., 1983; Wigfield and Eccles, 2000; Perez et al., 2019); cost, “Why don’t I want to do this?” (Barron and Hullerman, 2015; Perez et al., 2019).
EVCT suggests that students are more likely to engage in behaviors when they perceive a high likelihood of success (high expectancy), perceive the outcome to be important or valuable (high value: Wang et al., 2021), or perceive the task as not requiring ‘too much’ or causing them to give up engaging in other tasks (low cost; Fong et al., 2021; Lee et al., 2022). Conversely, students are less likely to engage in behaviors when they perceive a low likelihood of success (low expectancy), perceive the outcome to be unimportant (low value), or require ‘too much’ or causing them to miss out on other activities in which they want to participate (high cost). EVCT is used in educational settings to investigate and work to enhance motivation and engagement in learning by increasing the perceived value of a course, which in turn increases the likelihood of success of academic tasks (Eccles and Wigfield, 2020).
Achievement-goal theory of motivation
The achievement-goal theory (AGT) is a framework for understanding how individuals' goals and motivation influence their behavior and performance in achievement contexts (Elliot and McGregor, 2001). This theory uses the core concept of competence as a basis for developing goal orientations. Competence is broken into the two dimensions of definition (mastery and performance) and opposite valences (approach and avoidance; Elliot and McGregor, 2001). Competence is defined by the standard against which one can measure success (Elliot and McGregor, 2001). Individuals with mastery goals are motivated to develop their competence and improve their skills. They focus on learning and understanding new concepts and seek to master a task or activity. Individuals with performance goals are motivated to demonstrate their ability and competence to others. They focus on achieving favorable outcomes or avoiding negative ones, such as getting the highest exam score in the class or avoiding failure. Valence explains the subjective value a student places on the course (Elliot and McGregor, 2001; Lewis, 2018). Students show either a positive or negative valence. A positive valence corresponds to an approach goal orientation; a negative valence corresponds to an avoidance goal orientation. The goal orientations created by the valence and definition of competence set up a basic 2 × 2 achievement goal framework consisting of the four goal approaches: mastery-approach, mastery-avoidance, performance-approach, and performance-avoidance (Elliot and McGregor, 2001).AGT suggests that students' goals have important implications for their motivation, behavior, and performance. Students with mastery goals tend to be more intrinsically motivated, are persistent (Allen, 2020), and better able to adapt to challenges, while students with performance goals may be more susceptible to anxiety, fear of failure, and a tendency to avoid challenges that may jeopardize their performance (Elliot, 1999; Elliot and McGregor, 2001). For example, a student may choose a task they know they are able to accomplish successfully versus a task they may be less familiar with but affords them an opportunity for learning and growth, yet may lead to failure and shame. Research based on the AGT has shown that students who adopt mastery goals tend to have better academic outcomes, better problem-solving skills, and a more positive attitude towards learning, while students who adopt performance goals tend to focus more on grades and test scores, often at the expense of deep learning and understanding (Elliot, 1999; Lewis, 2018).
In a study done by Conley (2012) with middle school youth in a math setting, it was found that students were grouped by the pattern of their responses to multiple survey instruments designed to measure aspects of EVCT, including perceived costs, and also mastery and performance goal approaches among others. Some of the results reported in that study are that students display adaptive patterns when focused on mastery goals or multiple goal approaches (performance and mastery). However, students who displayed the performance avoidance pattern did not achieve the best results (Conley, 2012). Furthermore, this study also mentioned that perceived costs were more highly prevalent in less adaptive patterns of motivation (Conley, 2012). Understanding the interactions between perceived costs and goal approaches may be informative to the chemistry education community since these different subconstructs of motivation have not yet been investigated together in this setting. Utilizing cluster analysis to find student profiles of these motivational constructs, such as done by Conley (2012), may be beneficial in the general chemistry classroom.
Present study
The aim of the present study is to investigate how perceived costs and goal approach orientations of general chemistry students influence academic outcomes and success. We combined aspects of the EVCT and AGT to investigate perceived costs of general chemistry students within their general chemistry courses, as well as their desire to master the content or perform well when compared to other students. Although perceived costs have been theorized to be composed of four subscales as described by Flake and colleagues (2015), we obtained data for the task effort and emotional cost subscales utilizing the instrument they designed. Similarly, achievement goal orientation contains four subscales as described by Elliot and McGregor (2001), yet we only measured mastery approach and performance approach also utilizing the instrument they designed. The choice for using only select subscales of these two constructs is based on our desire to measure what seemed to be more strongly related to achievement based on theory and empirical evidence (Conley, 2012; Flake et al., 2015; Lewis, 2018). Moreover, considering the small size of the sample we could obtain at the institution where we conducted our study, we opted for just two subscales from each instrument. This decision aimed to simplify the statistical model and make it more feasible given our sample size. We used cluster analysis as a quantitative analysis of the survey results which showed the primary trends of students within the four categories of mastery approach, performance approach, task effort, and emotional cost. We then conducted interviews as a way to triangulate the survey results and provide additional evidence of the extent to which the clusters represent distinct groups of students.Research questions
We investigated two subscales of Perceived Costs (PC) and two subscales of Goal Approaches (GA) in order to find student profiles (clusters) that may explain aspects of student motivation and their relationship to achievement. The following research questions guided our investigation:• RQ1: To what extent can data on PC and GA in general chemistry be used to understand student motivational profiles?
• RQ2: To what extent are the student profiles based on the distinct combination of traits able to relate to academic achievement?
Methods
Participants
The research took place at a medium sized, primarily undergraduate university in the western United States. The data were collected during the Spring 2022 semester in first and second semesters of General Chemistry (GCI and GCII). We collected data from three sections of GCI and five sections of GCII; as a result 41.5% of the participants were enrolled in GCI, 58.5% were enrolled in GCII. The sections were taught by various instructors in the department. While some of the instructors offered a few points (<1% of the overall course grade) for students who completed the survey, others did not offer any incentive to complete the survey; however, all the students were informed that their participation was voluntary and they could skip answers if they wanted. 240 students took the survey. One student did not have complete answers to the survey; therefore, was removed from the data. Of the students who completed the survey, 46.0% were male, 53.2% were female, and 0.8% were nonbinary or preferred not to say. The sample was mostly White students (83.82%), followed by Hispanic students (9.96%), mixed race students (2.90%), Asian students (1.24%), African American/Black students (0.83%), Hawaiian/Pacific Islander students (0.83%), and students who chose not to answer (0.42%). These demographic trends follow the University's population demographics. Out of the students who took the survey at the beginning of the semester, 212 remained at the end of the course and took the American Chemical Society (ACS) final exam. Only the research team had access to the student data, although the instructors were provided a list of the students who took the survey in case they offered extra credit points. The students were informed that their instructors would be notified who took the survey but would not have access to their responses to the survey. In addition, we interviewed eight students who completed the survey. These interview participants were compensated with a gift card for their time in accordance with best practices. All data were collected following an approved IRB protocol and guidelines from the institution.Data collection
Survey
239 students completed the survey administered during the third week of the Spring 2022 semester. The data were collected from students in general chemistry courses taught by six different professors. The survey was administered after the first exam was completed and the withdrawal date had passed to eliminate responses from students who dropped the course early in the semester. Participating students self-reported how well they believed the statements under the four categories of emotional cost, task effort, mastery approach, and performance approach described how they felt about their motivation in their general chemistry course. Emotional cost was represented with items such as “This class was mentally exhausting” (Flake et al., 2015). The task effort subscale had items that had to do with the extent to which students had to invest time and energy into their course with items such as, “I have to put too much energy into this class” (Flake et al., 2015). Mastery approach was measured with items that evoked a sense of desire of knowledge, while performance approach was measured with items that described the students desire to compare their performance with their peers (Elliot and McGregor, 2001). Ratings were done on a scale of one to six for all four subscales, with one being ‘strongly disagree’ and six ‘strongly agree.’Interviews
Interviews were conducted with eight students. Students in the GCI and GCII courses were all given the opportunity to participate. An announcement on each of the participating courses’ LMS was submitted inviting all the students to volunteer to participate in an interview. The interested students were to email the research team. The interviews were done on a first-come, first-serve basis with one condition: the students had to have participated in the survey. The interviews were conducted after cluster analysis was performed with the survey data. The first two students who volunteered from each cluster were invited to participate in the interviews to provide additional evidence that our data contained four distinct clusters. These students volunteered to take part in a 30–45 minute interview and were given a $25 Amazon gift card for their participation. The interviews performed averaged 37 minutes in length, spanning from 23 minutes to 71 minutes.Analyses
Confirmatory factor analysis
To provide evidence of internal structure validity (Arjoon et al., 2013; AERA et al., 2014), we performed confirmatory factor analysis (CFA) with the data collected from the surveys. CFA was performed for a two-factor structure for Perceived Costs (PC) involving the task effort subscale and the emotional cost subscale. Additionally, a two-factor CFA was performed for Goal Approaches (GA) involving a mastery approach subscale and a performance approach subscale. The data-model fit was acceptable for both CFAs and the results are shown and discussed in the Appendix (Tables 3 and 4). This analysis was crucial to be able to continue our statistical analyses grouping the student responses together in the subscales discussed (Rocabado et al., 2020).Reliability
Evidence of reliability was pursued using the McDonald's Omega value as described by Komperda et al. (2018). Omega values are best when they approach 1.000 and much like Cronbach's alpha (Cronbach, 1951; Cortina, 1993), values above 0.700 indicate good reliability. In Tables 3 and 4 we indicate the factor reliability for each subscale. The values shown in these tables indicate strong reliability for each subscale with a range of reliability values of 0.913–0.923 for PC and 0.780–0.808 for GA.Cluster analysis
Cluster analysis was utilized to examine the patterns of responses for all four subscales. Through this investigation, student profiles would emerge to explain the levels of PC and GA that students experienced by grouping student responses with similar patterns; maximizing similarities between cases within a group and minimizing similarities between groups (Clatworthy et al., 2005, Lewis, 2018). Hierarchical cluster analysis was first employed in SPSS version 28.0.0. This method allows for an exploration of the patterns found in the data. This analysis produced several choices to consider that included three to seven clusters. We evaluated each of these possibilities. The three- and four-cluster solutions gave student profiles that were not in line with theoretical reasoning. Furthermore, these cluster solutions lacked clear distinctions, making them challenging to explain. The averages, while different among groups, overlapped significantly due to the substantial standard deviations. The six- and seven-cluster solutions provided several clusters that contained sample sizes that were too small (only a few students) and were not informative for those groups. Therefore, the solution that we decided to investigate is the five-cluster result. However, it is important to note that every result contained a cluster composed of a single student who did not fit with any group. We decided to treat this data point as an outlier and proceed with four clusters that produced distinct student profiles that could be explained with the theoretical background as well as contained appropriate sample sizes. Then, K-means clustering was performed (in SPSS version 28.0.0), which is a step recommended to refine the solutions found in hierarchical cluster analysis (Clatworthy et al., 2005). The results presented in this manuscript come from the five-cluster solution of K-means cluster analysis of student responses to statements categorized under the four topics of interest: emotional cost, task effort, mastery approach, and performance approach.Removing the single case cluster as an outlier, the four remaining clusters will be presented in this manuscript and were categorized as their averages differed from the total average in the sample. For example, if a cluster displayed averages higher than the total average, this cluster would be deemed a “High-all” cluster. While a cluster that displayed higher averages than the total in PC, but lower averages than the total in GA it would be deemed a “High-Low” cluster.
Analysis of variance (ANOVA)
In general chemistry courses at this institution the students take the American Chemical Society (ACS) exams as their final exam. We were able to obtain students’ ACS raw scores through the university data system. We performed an Analysis of Variance (ANOVA) test with the cluster membership and course, either General Chemistry I (GCI) or General Chemistry II (GCII), as the independent variable and the ACS raw score as the dependent, continuous variable. This test had the goal of providing insight as to whether student profiles (clusters) could be related to their performance in the course. It is important to note that only 212 students took the final exam from our original sample of 239. Thus, this analysis was done only with this subsample. The results of this test are found in the Appendix (Table 5).Analysis of interviews
The interview data were analyzed first using thematic analysis with codes that were in line with the constructs measured with the survey (Wilkinson et al., 2004). Thematic analysis was performed to identify topics from the survey warranting further investigation in interviews, to probe for costs and goal approaches that were correlated to each cluster that emerged from the analysis of survey responses. Later, open coding was performed to determine themes or topics highlighted in the interview that were not specific to emotional cost, task effort, mastery, or performance goal approaches, since the interviews allowed students to more freely express their views and feelings. The interview data were coded by the first author. The corresponding author coded significant portions of the data independently. Together the two researchers achieved 75% matching codes initially, and upon reviewing the discrepancies agreed fully on the codes. The interview protocol as well as a code list can be found in the Appendix.Results
We ran descriptive statistics, measuring the average scores for each factor (task effort, emotional cost, performance approach, and mastery approach) for the entire sample. As a whole, the sample displayed a normal distribution, except for mastery, which displayed a right-skewed distribution. Cluster analysis produced five clusters, one containing only one student – which we deemed an outlier and removed from further analysis – and four others, which were evaluated with respect to the total average. Table 2 in the Appendix shows the average responses within each category for clusters as well as the total average.Clusters were labeled based on their averages in comparison to the total average. Interpretations of survey responses led to cluster labeling. Subsequent interviews served as a qualitative description of the survey responses, shedding more light onto why each cluster's averages for the four categories occurred. Interviews supported the idea of four distinct groups of students. The clusters were finally labeled as follows: Cluster 1 “High PC, Low GA”, Cluster 2 “High-all”, Cluster 3 “Low-all”, and Cluster 4 “Low PC, High GA”. Fig. 1 shows the plotted mean values of the factors for the four clusters, along with the total average of each factor.
 | ||
| Fig. 1 Plotted averages of the four factors (task effort, emotional cost, mastery, and performance) for the four clusters along with the total sample average. Y-Axis represents the average for the survey results. Starts at 2.0 because no cluster average was below 2.0. The x-axis represents the different factors measured by the survey. Lines connecting the points show the trends of the clusters across the various factors. Cluster 1 high PC, low GA is represented in blue dots using diamonds (N = 36). Cluster 2 high-all, are shown in red short dashes and dots using triangles (N = 78). Cluster 3 low-all, is represented in black long dashes with two dots using squares (N = 58). Cluster 4, low PC, high GA, is shown in a green continuous line using circles (N = 66). The total average is represented in gray dashes. | ||
Overall high trend of mastery
We observed an overall “ceiling effect” of the mastery GA factor for all of the clusters. The total average for mastery (5.26 out of 6) was the highest total average of the four factors. This obvious trend of high mastery across clusters can be explained by social desirability. Students do not want to admit to their peers, professors, or even themselves that they don’t place importance in wanting to learn and master the material. Since the participants were interviewed by another student and not an instructor, many students were more willing to state in the interviews that, while they viewed a mastery goal as important, actually meeting this goal was not important, or feasible. A student in cluster 3 said, “This feels bad to say, but like I don’t feel like it's necessary to have that perfect mastery over it.” Apologizing, the student demonstrates how many students feel pressured or obligated to say they think that a mastery goal is important and desirable. The more honest views of mastery approach provided by the interviewed students allowed us to better scale the quantitative values for mastery-related survey responses.Cluster 1: high PC, low GA
While the survey results for task effort and emotional costs were supported by the interviews, the survey responses for mastery approach were somewhat conflated. The students who were interviewed from cluster 1 reported that they didn’t view mastery of the material as achievable nor important, though they still wanted to succeed and perform well. While cluster 1 did have the lowest survey average for mastery, they still had an average of 4.75 on a scale of one to six. Therefore, while their mastery goal was correctly reported, their perceptions of their ability to actually master the material were quite low. A student said “I mean I've like tried pretty hard for all my scores and I still am not doing as well as I'm hoping. But I think that's just to do with like the course, cuz it's very hard.”
The responses for performance for this cluster were supported. Cluster 1 produced the lowest average of the four clusters for performance approach in the survey. The students that were interviewed expressed wanting to do well but not feeling that they could achieve their desired success; therefore, comparing their performance with other students in the class was not an achievable goal for them. During the two different interviews for cluster 1, the students repeatedly expressed that they felt they understood the material and were putting in a significant amount of effort. However, when it came time to test, they expressed not being able to understand what was being asked of them or no longer understanding the material. This inability of the students to believe they can apply the material they learned on an exam not only supports the idea that performance goals seem not achievable for them, but also strongly supports the survey responses for high task effort and emotional cost. The students detailed how these feelings during, and about, their chemistry exams increased their stress levels, causing them to be overwhelmed by the course material, leading to high PC.
In addition, both students interviewed expressed similar views about the applicability of the material. The perceived level of applicability by students appeared to be tied to each cluster, as each cluster expressed differing views. Cluster 1 students emphasized multiple times that they did not feel the material was applicable to their future career goals and only slightly to future courses they will take. A student said, “Yeah. I mean, it's just like, it's important to know if I take another chemistry class, but like other than that I feel like I don't really have to know.” Students who held these views did not believe the course material needed to be completely mastered because it didn’t apply to them as much as other courses. Furthermore, applicability or utility as part of the value aspect of EVCT can be negatively related to PC; thus, students who don’t see the utility or applicability of what they learn can perceive a greater cost to learning that material, as seen through the interviews of students in this cluster.
Cluster 2: high-all
One of the students interviewed from cluster 2 consistently used the phrase “shoved down your throat” when discussing the content of the course. The frequency of which this phrase was used emphasizes the perception of high task effort and emotional cost indicated in the cluster's PC average score. Another topic discussed by the students was about the excessive time commitment and high stress levels experienced in their general chemistry course and how they felt as though they could not place the necessary amount of time into learning the content, which in turn further increased their stress levels. An interesting topic brought up by both students is how they felt like chemistry, as a subject, did not come to them as easily. A student explained, “I’m sure there's somebody that leaves the classroom and goes and does half the homework in 30 minutes… But I think for me, the reason why it takes long is that I’ve never done it before.” They described how hard they felt it was to actually grasp the material and understand it to an appropriate level. Another student shared, “But in one semester I feel like you can barely learn like half of the material, like effectively”.
Survey responses showed high averages of mastery approach and performance approach, while the high performance was supported by the interviews, the high mastery was conflated again. The explanations given by the two interviewed students from cluster 2 about their viewpoint on mastery provided insight on the discrepancies between interview and survey responses pertaining to mastery approach. The interviewed students disclosed that they viewed a significant portion of the material taught in their general chemistry course as being “useless to their future” and, therefore, was not worth attempting to master. While having the goal of mastery was important as seen in the survey, in reality these students did not see this goal as achievable due to the circumstances of the course such as the time frame and content load. Performance, however, was deemed important to the students interviewed. A student mentioned, “So the only thing that keeps me going is wanting to get an A.” They explained the importance of their grade and passing the course for their futures; therefore, their class standing when compared to others was an important motivator, inadvertently causing the students to shed light on a reason they viewed the course as having such a high emotional cost. One student shared, “I feel, especially when I'm like taking the test, like if I'm taking the test and someone finishes, I just mentally check out.”
Cluster 3: low-all
The diversity in responses was not initially seen during the interviews. During the beginning of the interview, both students expressed thoughts and feelings about their general chemistry course that would be expected from cluster 3 based on the survey responses. One student stayed consistent in these views, providing explanations that supported the low-all characterization of cluster 3. The student described how chemistry itself is not a huge stressor in their life – it is a difficult class but not necessarily more overwhelming than others. They then explained how they viewed mastery goal approach, it being a “give and take.” This student explained that if they didn’t think mastering a topic was worth its cost, they wouldn’t put in the extra effort for it, while content that they viewed relevant would become a priority to understand fully. This student had a positive attitude and outlook on the class, wanting to do well in the course and master the necessary content, but not placing a significant weight on feeling stressed or overwhelmed by thinking they had to learn every detail. This student also expressed they didn’t feel the need to compare their performance to other students in the class, and alluded to a more intrinsic level of motivation, “Like I don't know if I'm necessarily concerned with mastering the material, but I think more often than not, I'm learning for the sake of learning rather than for the sake of passing.”
In contrast, the other student interviewed from cluster 3 began to give explanations in the latter half of their interview that indicated they did not actually agree with the low-all statements of their cluster. Since the student gave answers supporting the factor averages in the beginning of the interview, it is likely that this student wanted to have a positive attitude about the course at the beginning of the semester when the survey was administered, but by the time the interviews were performed, this student had experienced higher levels of task effort and emotional costs associated with general chemistry. This was shown when the student said, “I think chemistry, like since I’ve started college, like chemistry has been my most stressful course.” They also went on to discuss how they feel as though their general chemistry course takes up the majority of their time, causing them to miss out on activities (another aspect of PC not thoroughly explored in this study; Flake et al., 2015).
Cluster 4: low PC, high GA
While other clusters discussed feeling as though the content was being presented too quickly to master it, the students in cluster 4 described feeling like the pace of the course was “too slow”, leading them to occasionally feel bored when going to lecture. An interesting insight that was brought up by one of the students was that they felt so confident in their ability to perform well in the course that there were instances they determined the amount of time and effort required to complete certain problems or assignments wasn’t worth it and that not doing these tasks wouldn’t substantially impact their final grades in the course. This is demonstrated by the following statement made by the student: “I don’t want to do any of this work because I already understand it. So, I haven’t really been doing the homework because, I’m like, my grade's good enough, I can take a few hits.”
Comparison of cluster membership and ACS scores
To investigate research question 2 we collected students’ ACS raw scores at the end of the semester since all general chemistry students are required to take it as their final exam for their course. This exam contains 70 multiple choice questions and students are given 110 minutes to complete the exam. The raw score presented here is the number of questions the students got correct out of the possible 70. As explained before, only 212 students were retained in the course and took this exam because 27 students from the original sample had dropped the course or were not present for the final exam, which would imply an unofficial withdrawal (UW), a withdrawal (W), or an incomplete (I) for the course. One of these students was the outlier that did not belong to any of the clusters. Interestingly, the students who did not take the final exam were evenly distributed among clusters 1–3. However, in cluster 4, students dropped the course at a lower rate when compared to the other clusters. Specifically, four students (11.1%) from cluster 1, ten students (12.8%) from cluster 2, eight students (13.8%) from cluster 3, and four students (6.1%) from cluster 4 did not participate in the final exam and, thus, are shown as having dropped the course. This interesting trend matches the expectation that students who perceive high costs in their course, such as in clusters 1 and 2, could have a lower retention rate than students displaying lower perceived costs (cluster 4). Cluster 3 does not follow the same logic, having the highest drop rate of all clusters. This result could be explained by the lower mastery and performance goals of these students. Since these goals were not as important for cluster 3 students, it is possible that they were not as invested in the course as the students in the other clusters and eventually dropped the course even if their grades on average were on par with students in clusters 1 and 2. Another explanation for this result is that students in cluster 3, as indicated by one of the interviewees, may have experienced higher levels of PC throughout the semester, leading to outcomes similar to those of students in clusters 1 and 2.Another noticeable trend as seen in Table 1 is the average ACS scores for Clusters 1 and 4 in both courses. These two clusters displayed opposing views in both PC and GA and also the opposite averages in their ACS scores. While Cluster 1 perceived high costs in the class, both of their mastery and performance goal approaches were the lowest from any group. During the interviews these students thought that having mastery goals was important but also mentioned that actually achieving that goal was not feasible. This group had the lowest ACS average scores in both courses. In contrast, students in cluster 4 had the opposite PC and GA profile than students in cluster 1 with exactly opposite outcomes: highest ACS average scores and also highest retention rate.
| N | Ave. ACS raw score | Standard deviation | |
|---|---|---|---|
| Cluster 1 GCI | 12 | 37.17 | 11.13 |
| Cluster 2 GCI | 25 | 41.80 | 11.45 |
| Cluster 3 GCI | 25 | 43.00 | 12.83 |
| Cluster 4 GCI | 26 | 45.50 | 11.64 |
| Cluster 1 GCII | 20 | 42.75 | 8.27 |
| Cluster 2 GCII | 43 | 43.95 | 10.85 |
| Cluster 3 GCII | 25 | 45.68 | 6.87 |
| Cluster 4 GCII | 36 | 47.25 | 9.76 |
We performed an analysis of variance (ANOVA) test to see whether there were significant differences between the clusters’ average ACS scores. The results of this test yielded no evidence of statistical significant differences between the groups described in Table 1 (F(3, 212) = 7.733, p = 0.134). A table with the ANOVA results can be found in the Appendix.
Preparedness
To understand student outcomes and success in general chemistry, we examined their PC and GA associated with the course. We found that preparedness may be a key factor in students’ success and positive outcomes, although we acknowledge that motivation and its relationship to achievement outcomes is nuanced and complex. However, we did observe that students who had previous experience with course material were more prepared going into their undergraduate general chemistry course. The two students interviewed (one from GCI and one from GCII) from cluster 4, explained that they had taken similar courses to their current general chemistry course in high school or college. We found this information interesting because the students that were interviewed for the other clusters explicitly mentioned they did not have previous experience or they talked about their previous exposure to chemistry as being “limited” or “not helpful.” In contrast, students in cluster 1 (high PC, low GA) voiced their concerns about the high content load, fast-paced environment of the course, and their perception of inability to learn the material to their desired level. These concerns led these students to believe that the course was “too difficult” and “emotionally taxing.” When the interviews were complete, we noted that one of the main differences between the interviewed students in clusters 1–3 and the two students in cluster 4 was the level of preparedness they had prior to entering their course and ultimately how they eventually performed in the course.Discussion and implications
Student motivation in a chemistry classroom is a worthwhile topic of investigation due to the challenging nature of the field. Many researchers have investigated student motivation in K-12 (e.g., Ko and Marx, 2019; Gong et al., 2023) as well as in higher education (e.g., Pratt and Raker, 2020; Lee et al., 2022); however, only a few have focused on studying PC in undergraduate chemistry (i.e., Lee et al., 2022). This study provides a thorough investigation on the different perspectives of students that have a variety of goals, perceptions, and different levels of preparation. By utilizing quantitative methods we were able to investigate student's self-reported PC and GA profiles and their relationship to their achievement in the class. In addition, student interviews supported the various student grouping that emerged from cluster analysis of the survey data. One important concept we learned is that when students come prepared with previous knowledge of chemistry, they can focus on their mastery and performance goals rather than the barriers experienced by perceived costs. The students don’t feel overwhelmed or frustrated when they don’t understand certain concepts. On the other hand, when students are less prepared, it is likely they focus on task effort and emotional costs and their mastery and/or performance goals seem less achievable. Ultimately some of these students can be the ones who withdraw or fail, which can lead to switching majors outside of STEM (Seymour and Hewitt, 1997; Seymour and Hunter, 2019).The four clusters that were found through statistical analyses were eye-opening. The interviews supported the groupings and gave us additional insights on those student profiles and were able to add more detail to the student struggles and perspectives. Cluster 1, the high PC, low GA students, were those students who felt the class was going too fast and they couldn’t fully grasp the material to a comfortable level; thus, they were not sure they would be successful in achieving their goals. Every classroom will have students who display this profile. Therefore, addressing the particular needs for this group of students is important. While most universities have resources for students to utilize outside of the classroom, it is likely that these students might not know how to use these resources, or might not use them often or in a meaningful way. Therefore, it is important to learn about the costs and barriers that students face to help break those barriers and give students resources they feel comfortable in using.
Cluster 2 students (high-all) are an interesting group of students. Perhaps we see them as the ‘overachievers,’ who want to accomplish everything. The interviewed students in this group reported their need for a “shiny transcript” because of their goals as future healthcare professionals. Quantitatively, these students reported high PC and GA. Their high mastery and performance goals led them to feel overwhelmed in their chemistry course and to believe too much is required of them. In the interviews, the students’ feeling of being overwhelmed in a high-stress environment were supported. The students shared how, in theory, they would like to be able to learn and master everything (high mastery goal approach), but don’t believe it is possible nor believe it is useful or necessary. These students were significantly more focused on their grades and their class ranking when compared to others (performance goal approach) than on understanding the material for their future careers. While students reported a higher mastery approach, in the interviews it was shown that these students had switched their focus to their performance approach goals more, often because they didn’t seem to think the material they were required to learn was useful to their overall future goals. This issue is seen in many classrooms. Wang and colleagues (2021) report positive attitude and achievement outcomes from a simple intervention designed to help students self-generate utility value of concepts learned in class. This intervention, when done purposefully, could lead to students in this group (and other groups as well), to see the value of the material they are learning and focus more on understanding rather than just their grade. This shift of focus could help ease some aspects of the high PC in the class.
Cluster 3, the low-all group, was composed of students who understood that chemistry is an intense course that requires effort and time. Yet, these students displayed a level of comfort with thinking they did not need to place too high of a priority on their mastery or performance goals. This perspective helped students seemingly feel at ease in the class at the beginning of the semester. However, in the interviews we learned that this perspective is not true for all the students. One of the interviewees shared that the course was stressful and difficult, which was contrary to the trend we saw from the survey. Although the realistic perspective of these students could be helpful, it is important to note that even though they may self-report lower levels of PC at the beginning of the semester, it is possible that those may change later on. Future studies should monitor students’ levels of PC and GA throughout the semester and observe points in which meaningful interventions could be embedded in a class to mediate the rise in PC.
Cluster 4, the low PC, high GA group, was composed of students who seemingly thought the costs of taking their course was low and mastering the material as well ranking top of the class were worthy goals. During the interviews we observed these trends very clearly with two students who had previous experience in chemistry courses. This previous experience led them to feel like they could place priority in both mastery and performance goals, and feel like they could achieve both of them without the interference of the PC barriers. One of these students even said they did not need to do the homework because their grades were high enough and they could “take a few hits.” Another student was also bored in class thinking that lecture went “too slow.” These feelings are in contrast to students in other clusters who felt the pace of the class was too fast, the content was “shoved down [their] throat,” and feeling overwhelmed because “they had never done it [chemistry] before.” Having students that are not challenged enough in a class can also be demotivating. It is important to keep all students engaged at their level for optimal growth and motivation (Ryan and Deci, 2017). This is very difficult to do as students have vastly different levels of preparation and profiles. However, while we do not want to leave our struggling students behind, we also would not want to demotivate our students who come prepared and perform well with little effort. Designing a curriculum that engages students at their level can be very challenging, and we call for the chemistry education community to continue to work on a curriculum that is flexible and rigorous to provide the best experience for every student.
We comment on the interesting trends of cluster membership and its relationship to achievement and retention. First, the ANOVA test to investigate difference in achievement as measured by the ACS average scores yielded no evidence of statistical significant difference between any of the groups of students (divided by clusters and courses). However, we can clearly observe an interesting trend in both courses where cluster 1 displays the lowest ACS average scores and cluster 4 displays the highest ACS average scores. More investigations should be done about the role that PC and GA play in academic achievement in general chemistry and in other chemistry courses.
Additionally, an interesting trend in retention was observed in the different clusters. Cluster 1–3 had similar retention rates while cluster 4 saw higher retention rates in their courses. While clusters 1 and 2 displayed high PC from the beginning, cluster 3 did not. However, through the interviews we learned that there is a possibility for students to change their perception of costs in the class throughout the semester. If some of the students in cluster 3 experienced this shift, the higher PC could have led them to similar outcomes than their peers in cluster 1 and 2. Therefore, this result implies that students who experience higher PCs could eventually avoid chemistry or leave STEM all together (Seymour and Hewitt, 1997; Seymour and Hunter, 2019). This trend is likely to be similar across the college chemistry courses in the United States and across the globe. The investigation of PC is new in chemistry education. We call for more researchers and practitioners across the world to investigate PC in their own classrooms and projects and for the design of curriculum and interventions that can mediate the negative effects of these barriers on students' success and motivation.
Although motivation is a highly nuanced construct, and the way it relates to achievement is highly complexed and sometimes non-linear, we can see how student previous experience with chemistry may be a factor that can lower PC and allow students to focus on their mastery goals leading to better performance and higher retention as seen with the students that were interviewed from cluster 4.
Implications for researchers
In this study we utilized quantitative and qualitative methods to investigate PC and GA in general chemistry. We were able to better understand the quantitative data obtained from the survey by having students elaborate and give further insight through interviews. Based on these findings, we think it is appropriate to point out that some items on the GA instrument could have been interpreted in a range of ways by students. For example, the item “I want to learn as much as possible from this class” can mean a desire to master every concept in the class, or it can mean learning enough material to pass the course. Students shared a range of interpretations with this and other items. Thus, a review of these items is warranted for future studies. We call on researchers to continue to use both quantitative and qualitative methods when possible to interpret quantitative data based on instruments that are not often used in their context.Studies looking at perceived costs and goal approaches in general chemistry should be carried out in different universities and countries to better understand costs within different cultures and environments. In addition, perceived costs may be different in organic chemistry and upper division chemistry courses; therefore, investigating these constructs in these settings would be worthwhile.
We also call on researchers to use instruments that were thoroughly designed to measure these complex constructs. If such instruments don’t exist, interested researchers should develop such instruments to be used in a variety of settings with diverse populations (Rocabado et al., 2023). Adhering to The Standards (Arjoon et al., 2013; AERA et al., 2014) is imperative to the development of instruments that could yield useful and informative results, as well as valid and reliable inferences from those results.
Implications for instructors
The data collected provides a fascinating and important insight into student motivations and perceptions toward general chemistry. One of the significant findings was that previous experience with chemistry provided students with the resources to focus on their mastery and performance goals without many of the PC barriers. While it is impractical to require students in college general chemistry to have taken a chemistry course before in high school or college, the negative effects of PC are obvious for the students without this previous experience. In addition, we were able to observe that while many students stated a high importance in mastery goals, eventually they would focus more on performance goals due to their perception that chemistry is not useful. Therefore, focusing our efforts in effective interventions to mediate PCs is warranted. Instructors can encourage a more mastery-based approach by providing opportunities that allow students to demonstrate their knowledge without being heavily penalized academically. This can be done by implementing activities such as low point assignments that encourage students to be engaged without feeling stressed (Hulleman and Harackiewicz, 2021). Such activities provide a way for students who want extra practice to have access to it, while students who do not will not be academically penalized for not participating. In addition, instructors can help students to self-generate utility value of the content to counteract the negative effects of PC by implementing short assignments asking students to indicate the utility of what they’re learning in their lives (Wang et al., 2021).We call on instructors to be attentive to specific costs that their students may have. Students across the globe will likely experience perceived costs of taking a chemistry class. While some costs may be similar in many institutions across the globe, there will be some that are specific to a region or group of students. This creates the need to address distinct barriers for individual students or groups of students in our own spaces.
Limitations
We acknowledge the limitations of this study, which should be considered in the interpretation of the results. The limitations are centered around the chosen sample group. While the study included students enrolled in General Chemistry I and II across multiple sections taught by several different professors, the student ethnic and racial backgrounds were not diverse, reflecting the student population of the university. Therefore, this study did not investigate PC or GA based on subgroups of students of diverse backgrounds, yet we do believe that gathering such data would be informative and important. We hope that others can investigate additional populations with a larger range of racial and ethnic diversity. The university at which this study was conducted is a medium-sized, primarily undergraduate university, with small class sizes. Based on our setting, we had a relatively small sample size. Finally, the number of students interviewed was also small. Talking with more students would be ideal for a greater range of experiences. Our findings showed that interviews were critical for better interpretation of the quantitative survey results. Including more interviews for each cluster would allow for better understanding of the constructs and the clusters.Data availability statement
The data that support the findings of this study are available on request from the corresponding author, GAR. The data are not publicly available due to information that could compromise the privacy of research participants (i.e., course sections, etc.).Conflicts of interest
None to report.Appendix
Descriptive statistics
| Cluster number | Statistical measures | Task effort (PC) | Emotional cost (PC) | Performance (GA) | Mastery (GA) |
|---|---|---|---|---|---|
| Note. Sample sizes for clusters 1–4 add to a total sample size of 238 participants that answered every question. The single missing participant fell under their own, single-person cluster, not fitting with any of the above four clusters, therefore deemed as an outlier and removed from the data. Other statistical analyses will be done with the 238 remaining participants. | |||||
| 1 | N | 36 | 36 | 36 | 36 |
| “High PC Low GA” | Mean | 3.82 | 4.36 | 3.06 | 4.75 |
| Std. Dev | 0.61 | 0.67 | 0.68 | 0.77 | |
| 2 | N | 78 | 78 | 78 | 78 |
| “High-all” | Mean | 3.90 | 4.74 | 5.08 | 5.50 |
| Std. Dev | 0.73 | 0.78 | 0.57 | 0.54 | |
| 3 | N | 58 | 58 | 58 | 58 |
| “Low-all” | Mean | 2.63 | 2.59 | 3.71 | 4.87 |
| Std. Dev | 0.68 | 0.60 | 0.75 | 0.63 | |
| 4 | N | 66 | 66 | 66 | 66 |
| “Low PC High GA” | Mean | 2.36 | 2.39 | 5.13 | 5.63 |
| Std. Dev | 0.75 | 0.77 | 0.52 | 0.40 | |
| Total | N | 239 | 239 | 239 | 239 |
| Mean | 3.16 | 3.50 | 4.45 | 5.26 | |
| Std. Dev | 1.00 | 1.30 | 1.03 | 0.70 | |
Confirmatory factor analysis
Herein we present the results of the CFAs performed for the entire sample for the two constructs under consideration: perceived costs (PC) and goal approaches (GA). Each of these constructs is described by two subscales, namely, task effort and emotional cost for PC and mastery approach and performance approach for GA. Tables S1 and S2 show data-model fit indicating acceptable model fit (Hu and Bentler, 1999). We want to point out that the RMSEA displays irregular behavior with short instruments like these, often indicating poor fit (Kenny et al., 2015). These results support the additional statistical analyses performed in the manuscript at the subscale level.In addition, we report Omega as a measure of reliability for each subscale. Omega values closer to 1 indicate strong reliability, with values higher than 0.7 being acceptable. All of the values shown indicate acceptable to strong reliability (Tables 3 and 4).
| χ 2 | df | p | CFI | SRMR | RMSEA | Omega TE | Omega EC | |
|---|---|---|---|---|---|---|---|---|
| Spring 22 | 171.007 | 34 | <0.001 | 0.907 | 0.088 | 0.130 | 0.913 | 0.923 |
| TE = task effort. EC = emotional cost. | ||||||||
| χ 2 | df | p | CFI | SRMR | RMSEA | Omega MA | Omega PA | |
|---|---|---|---|---|---|---|---|---|
| Spring 22 | 53.899 | 19 | <0.001 | 0.934 | 0.060 | 0.087 | 0.780 | 0.808 |
| MA = mastery approach. PA = performance approach. | ||||||||
Interview protocol
This interview protocol is semi-structured. The questions below will be part of the interview, but additional questions may arise as the conversation progresses. The interviewer will be able to probe further interesting concepts that may arise from the student's responses.Introduce yourself.
Tell a little bit about the project.
Have students read the consent form, and ask any questions.
Get a consent form signed.
Start the recording.
Questions:
1. Ask student's major and future career goals (to build rapport)
2. What chemistry classes have you taken so far?
a. Did you take chemistry in high school?
3. What are some of the things you like about your chemistry course?
4. What are some things you wish were different about your chemistry course?
5. About how much time do you spend on your chemistry course?
a. Is this amount of time appropriate or do you wish you had more time?
b. What are some things that prevent you from spending more time in your chemistry course?
6. What is your motivation behind the amount of time and effort you spend in this course?
7. How is your effort in this course related to your future career goals?
8. How important is getting a good grade in this course? Why?
a. Do you usually compare your grades with others in the course? Why/Why not?
9. How important is learning the material in this course for you? Why?
Stop recording. End of interview.
Thank the student and provide the gift card.
Have the student sign a receipt (Table 5).
| Sum of squares | df | Mean square | F | Sig. | |
|---|---|---|---|---|---|
| Between groups | 1246.604 | 7 | 178.086 | 1.611 | 0.134 |
| Within groups | 22![[thin space (1/6-em)]](https://www.rsc.org/images/entities/char_2009.gif) 552.014 |
204 | 110.549 | ||
| Total | 23798.618 |
211 |
The ANOVA test did not reveal evidence of statistical differences between the different groups in this test; therefore, post hoc comparisons between groups was not necessary.
Instruments used for this study
Perceived cost instrument (from Flake et al., 2015)
Items for emotional cost1. This class is mentally exhausting
2. I feel too anxious about this class
3. This class is emotionally draining
4. I worry too much about this class
5. This class makes me feel too anxious
6. This class is too stressful
7. This class takes too much out of me emotionally
8. This class is too frustrating
Items for task effort
1. This class demands too much of my time
2. This class is too demanding
3. I have to put too much energy into this class
4. This class is too much work
5. This class takes up too much time
6. This class takes too much effort
Achievement goal instrument (from Elliot and McGregor, 2001)
Items for performance approach1. It is important for me to do better than other students
2. It is important for me to do well compared to others in this class
3. My goal in this class is to get a better grade than most of the other students
Items for mastery approach
1. I want to learn as much as possible from this class
2. It is important for me to understand the content of this course as thoroughly as possible
3. I desire to completely master the material presented in this class
Code lists from student interviews are given in Table 6.
| Subconstructs | Themes | Examples |
|---|---|---|
| PC – task effort | Chemistry takes time | “Out of all my classes, this is the class I spend the most time on.” – C1 |
| “Unless you put in a lot of time, you're not gonna be able to do well” – C2 | ||
| “That's another hard thing about the course is it's so much material in such a short amount of time” – C2 | ||
| “But I think for me, the reason why it takes long is that I've never done it before.” – C2 | ||
| “I don't feel like I have enough time to retain anything.” – C2 | ||
| “Like my day would go a lot different if I wasn't spending so much time, like with homework and studying and stuff.” – C3 | ||
| Chemistry is hard | “Like we kind of have to take the principles that we're supposed to have known and combine them together and solve the problem. Which has been a little rough.” – C1 | |
| “I mean I've like tried pretty hard for all my scores. And I still am not doing as well as I'm hoping. But I think that's just to do with like the course, cuz it's very hard,” – C1 | ||
| “I've worked so hard in trying to understand chemistry” – C2 | ||
| “Just kinda shoved down your throat and it's kind just like, get, memorize this as quick as possible, or you are gonna fall behind fast” – C2 | ||
| “There's just certain things that it does take so much effort to try and learn or memorize for that matter.” – C3 | ||
| “Most of the stuff we're learning is stuff that I learned back in that college class that it took before. So it's all kind of like familiar, so I don't really need to study much more.” – C4 | ||
| “But I must have retained more than I expected from AP chemistry, which was nice and it's come decently easily to me, which is great.” – C4 | ||
| “It's so nice to not have to struggle to understand it.” – C4 | ||
| The pace is too fast or too slow | “Just kinda shoved down your throat and it's kind just like, memorize this as quick as possible, or you are gonna fall behind fast” – C2 | |
| “I think my biggest complaint, and I think this might be just like only my complaint because I've already kind of learned the material, but I feel like the pace is too slow.” – C4 | ||
| PC – emotional cost | Chemistry is stressful | “It's stressful in the sense that I don't know exactly what's going to be on there, so I don't feel like I know what to study.” – C1 |
| “Chemistry is definitely my most stressful class, because I do have it like every day except for Thursday. And so I feel like my emotional toll like just builds up during the week and it's like, oh man, I have to do this again.” – C1 | ||
| “I can see the stress on the professor as well as the stress on all the students.” – C2 | ||
| “I think chemistry, like since I've started college, like chemistry has been the most stressful course.” – C3 | ||
| “I feel like for me, I'm not, I don't do a lot of stressing about it [chemistry].” – C3 | ||
| “If I feel like something's not worth doing for my grade and for my stress, I just don't do it.” – C4 | ||
| “I think I'm pretty good at managing my stress and not getting emotionally overwhelmed and making sure I still get this stuff done that I need to.” – C4 | ||
| Chemistry brings you down | “Like I almost feel like it's more just like a shot in the dark” – C1 | |
| “If I'm taking the test and someone finishes, I just mentally check out.” – C2 | ||
| “Like it's pretty much just a smack in the back of the head to go, oh look, you actually suck at this.” – C2 | ||
| GA – mastery approach | Desire to understand | “I'm not necessarily concerned with mastering the material, but I think more often than not, I'm learning for the sake of learning rather than for the sake of passing.” – C3 |
| “I really like I guess the appeal that science has, I just like learning things, like understanding things. And for me that's really cool and that's really fun to like, know how those things work.” – C3 | ||
| “I want to get the good grade because that will help me get into medical school, but the way I want to do that is by understanding the material. I figure if I figure out how to do the material, that's what'll get me the good grade.” – C4 | ||
| Desire to succeed | “I think it's about where I'm comfortable with where I'm at. Like maybe I get like a B minus in class. Like that's to me, that's still passing.” – C3 | |
| “I love a good challenge. And so I'm going into o-chem. Like I will get that A just cuz it's a challenge and I want to succeed.” – C4 | ||
| GA – performance approach | Desire to pass the course | “I think I used to just want to have a really good GPA and then I think chemistry ruined it in the first place because I just could not keep up at all. And so that was just, and so now I just kind of am just like, okay, I just want to pass the class. I don't care about the material. I just wanna pass the class and move on.” – C2 |
| “If I feel like it's very applicable to me and what I'm interested in and wanting to do, then I'm all over mastering it.” – C2 | ||
| Desire to get a good grade for show | “I want a good grade. So I try to study a little bit harder.” – C1 | |
| “So the only thing that keeps me going is wanting to get an A.” – C2 | ||
| “And if it's just nonsense that I'll never use it in my life, I just, I just want to get through it and get a good grade. So it looks good.” – C2 | ||
| “I'm more afraid about not getting the work done and turned in, to get a hundred percent so that way I can keep a good grade versus just being able to sit down and figure out and learn something.” – C2 | ||
| Comparing grades with others | “I feel like they have a better like overall background, knowledge of chemistry. And where I don't. And so it's been like kind of a struggle for me.” – C1 | |
| “I sit next to two guys and they usually get better scores than me. Like that's kind of like, it just makes me kind of sad cause I'm like, oh I thought I aced that test. And then I didn't. Like what the Heck is wrong with me kind of thing.” – C1 | ||
| “I personally don't compare myself to others.” – C3 |
Acknowledgements
The authors wish to acknowledge Southern Utah University's Department of Chemistry and Physics and the College of Natural Sciences for the financial support in this project through the Gibson Maxwell Fellowship. Additionally, we thank Dr Scott Lewis for training and help with cluster analysis. We also wish to acknowledge Aliza Conder, Hayley Kramer, and Alexandria Wood for their work early in this project, and all the student participants as well as the professors who allowed collection of data in their classrooms.References
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